Package 'manynet'

Description

This function makes it easy to get an overview of available data:

• table_data() returns a tibble with details of the network datasets included in the packages.

Usage

table_data(pkg = c("manynet", "migraph"), ...)

Arguments

Examples

table_data()
# to obtain list of all e.g. directed networks:
table_data(pkg = "manynet", directed)
# to obtain overview of unique datasets:
table_data() %>% 
  dplyr::distinct(directed, weighted, twomode, signed, 
                 .keep_all = TRUE)

Description

One-mode network collected by McNulty (2020) on the connections between the Friends TV series characters from Seasons 1 to 10. The fict_friends is an undirected network containing connections between characters organised by season number, which is reflected in the tie attribute 'wave'. The network contains 650 nodes Each tie represents the connection between a character pair (appear in the same scene), and the 'weight' of the tie is the number of scenes the character pair appears in together. For all networks, characters are named (eg. Phoebe, Ross, Rachel).

Usage

data(fict_friends)

Format

#> # Friends network
#> # A labelled, weighted, undirected network of 650 characters and 2976 scene
#> co-appearance ties
#> # A tibble: 650 x 1
#>   name        
#>   <chr>       
#> 1 Actor       
#> 2 Alan        
#> 3 Andrea      
#> 4 Angela      
#> 5 Aunt Iris   
#> 6 Aunt Lillian
#> # i 644 more rows
#> # A tibble: 2,976 x 3
#>    from    to weight
#>   <int> <int>  <dbl>
#> 1     8     9      3
#> 2     4    10      1
#> 3     8    12      1
#> 4     9    12      1
#> 5     2    14      1
#> 6     3    14      1
#> # i 2,970 more rows

References

McNulty, K. (2020). Network analysis of Friends scripts..

Description

Grey's Anatomy is an American medical drama television series running on ABC since 2005. It focuses on the personal and professional lives of surgical interns, residents, and attendings at Seattle Grace Hospital, later renamed as the Grey Sloan Memorial Hospital. Gary Weissman collected data on the sexual contacts between characters on the television show through observation of the story lines in the episodes and fan pages, and this data was extended by Benjamin Lind including nodal attributes:

• 'name': first and, where available, surname

• 'sex': F for female and M for male

• 'race': White, Black, or Other

• 'birthyear': year born (some missing data)

• 'position': "Chief", "Attending", "Resident", "Intern", "Nurse", "Non-Staff", "Other"

• 'season': season that the character joined the show

• 'sign': character's astrological starsign, if known

The data is current up to (I think?) season 10?

Usage

data(fict_greys)

Format

#> # Grey's Anatomy
#> # A labelled, undirected network of 53 characters and 56 hook-up ties
#> # A tibble: 53 x 7
#>   name               sex   race  birthyear position  season sign  
#>   <chr>              <chr> <chr>     <dbl> <chr>      <dbl> <chr> 
#> 1 Addison Montgomery F     White      1967 Attending      1 Libra 
#> 2 Adele Webber       F     Black      1949 Non-Staff      2 Leo   
#> 3 Teddy Altman       F     White      1969 Attending      6 Pisces
#> 4 Amelia Shepherd    F     White      1981 Attending      7 Libra 
#> 5 Arizona Robbins    F     White      1976 Attending      5 Leo   
#> 6 Rebecca Pope       F     White      1975 Non-Staff      3 Gemini
#> # i 47 more rows
#> # A tibble: 56 x 2
#>    from    to
#>   <int> <int>
#> 1     5    47
#> 2    21    47
#> 3     5    46
#> 4     5    41
#> 5    18    41
#> 6    21    41
#> # i 50 more rows

Author(s)

Gary Weissman and Benjamin Lind

Description

A network of 36 Lord of the Rings book characters and 66 interactional relationships. The ties are unweighted and concern only interaction. Interaction can be cooperative or conflictual.

Usage

data(fict_lotr)

Format

#> # Lord of the Rings
#> # A labelled, complex, undirected network of 36 characters and 66 interaction
#> ties
#> # A tibble: 36 x 2
#>   name     Race  
#>   <chr>    <chr> 
#> 1 Aragorn  Human 
#> 2 Beregond Human 
#> 3 Bilbo    Hobbit
#> 4 Celeborn Elf   
#> 5 Denethor Human 
#> 6 Elladan  Elf   
#> # i 30 more rows
#> # A tibble: 66 x 2
#>    from    to
#>   <int> <int>
#> 1     1     7
#> 2     1     8
#> 3     5     9
#> 4     1    10
#> 5     3    10
#> 6     9    10
#> # i 60 more rows

Description

Goele Bossaert and Nadine Meidert coded peer support ties among 64 characters in the Harry Potter books. Each author coded four of seven books using NVivo, with the seventh book coded by both and serving to assess inter-rater reliability. The first six books concentrated on adolescent interactions, were studied in their paper, and are made available here. The peer support ties mean voluntary emotional, instrumental, or informational support, or praise from one living, adolescent character to another within the book's pages. In addition, nodal attributes name, schoolyear (which doubles as their age), gender, and their house assigned by the sorting hat are included.

Usage

data(fict_potter)

Format

#> # Harry Potter
#> # A longitudinal, labelled, complex, directed network of 64 students and 544
#> support arcs
#> # A tibble: 64 x 4
#>   name              schoolyear gender house     
#>   <chr>                  <int> <chr>  <chr>     
#> 1 Adrian Pucey            1989 male   Slytherin 
#> 2 Alicia Spinnet          1989 female Gryffindor
#> 3 Angelina Johnson        1989 female Gryffindor
#> 4 Anthony Goldstein       1991 male   Ravenclaw 
#> 5 Blaise Zabini           1991 male   Slytherin 
#> 6 C. Warrington           1989 male   Slytherin 
#> # i 58 more rows
#> # A tibble: 544 x 3
#>    from    to  wave
#>   <int> <int> <dbl>
#> 1     2     2     2
#> 2     2     2     3
#> 3     2     2     5
#> 4     2     3     2
#> 5     2     3     3
#> 6     2     3     5
#> # i 538 more rows

References

Bossaert, Goele and Nadine Meidert (2013). "'We are only as strong as we are united, as weak as we are divided'. A dynamic analysis of the peer support networks in the Harry Potter books." Open Journal of Applied Sciences, 3(2): 174-185. doi:10.4236/ojapps.2013.32024

Description

The original dataset was put together by Erin Pierce and Ben Kahle for an assignment for a course on Bayesian statistics. The data included information on when characters died in the Song of Ice and Fire books, and some predictive factors such as whether they were nobles, married, etc. Shirin Glander extended this data set on character deaths in the TV series Game of Thrones with the kinship relationships between the characters, by scraping "A Wiki of Ice and Fire" and adding missing information by hand. There is certainly more that can be done here.

Usage

data(fict_thrones)

Format

#> # Game of Thrones Kinship
#> # A labelled, multiplex, directed network of 208 characters and 404 kinship
#> arcs
#> # A tibble: 208 x 10
#>   name          culture house  popularity Gender title birth death noble married
#>   <chr>         <fct>   <chr>       <dbl> <chr>  <chr> <int> <int> <lgl> <lgl>  
#> 1 Alys Arryn    <NA>    House~     0.0803 female ""       NA    NA FALSE TRUE   
#> 2 Elys Waynwood <NA>    House~     0.0702 female "Ser"    NA    NA TRUE  TRUE   
#> 3 Jasper Arryn  <NA>    House~     0.0435 male   "Eyr~    NA    NA TRUE  FALSE  
#> 4 Jeyne Royce   <NA>    House~     0      female  <NA>    NA    NA NA    NA     
#> 5 Jon Arryn     Valemen House~     0.836  male   "Eyr~   217   298 TRUE  TRUE   
#> 6 Lysa Arryn    <NA>    House~     0      female "Lad~   266   300 TRUE  TRUE   
#> # i 202 more rows
#> # A tibble: 404 x 3
#>    from    to type  
#>   <int> <int> <chr> 
#> 1     1     2 spouse
#> 2     2     1 spouse
#> 3     3     1 parent
#> 4     3     5 parent
#> 5     4     5 spouse
#> 6     5     4 spouse
#> # i 398 more rows

References

Pierce, Erin, and Ben Kahle. 2015. "Bayesian Survival Analysis in A Song of Ice and Fire".

Glander, Shirin. 2017. "Network analysis of Game of Thrones".

Description

This function adds a glossary item, useful in tutorials.

Usage

gloss(text, ref = NULL)

print_glossary()

clear_glossary()

Arguments

Description

This network consists of the blogosphere around the time of the 2004 US presidential election until February 2005. The 2004 election was the first in which blogging played a significant role. Ties were constructed from a crawl of the front page of each blog.

Political leaning is indicated as "Liberal" (or left leaning) or "Conservative" (or right leaning), sourced from blog directories. Some blogs were labelled manually, based on incoming and outgoing links and posts.

Usage

data(irps_blogs)

Format

#> # A labelled, complex, directed network of 1490 blogs and 19090 link arcs
#> # A tibble: 1,490 x 3
#>   name                       Leaning Source               
#>   <chr>                      <chr>   <chr>                
#> 1 100monkeystyping.com       Liberal Blogarama            
#> 2 12thharmonic.com/wordpress Liberal BlogCatalog          
#> 3 40ozblog.blogspot.com      Liberal Blogarama,BlogCatalog
#> 4 4lina.tblog.com            Liberal Blogarama            
#> 5 750volts.blogspot.com      Liberal Blogarama            
#> 6 95theses.blogspot.com      Liberal Blogarama            
#> # i 1,484 more rows
#> # A tibble: 19,090 x 2
#>    from    to
#>   <int> <int>
#> 1   267  1394
#> 2   267   483
#> 3   267  1051
#> 4   904  1479
#> 5   904   919
#> 6   904  1045
#> # i 19,084 more rows

References

Adamic, Lada, and Natalie Glance. 2005. "The political blogosphere and the 2004 US Election: Divided they blog". LinkKDD '05: Proceedings of the 3rd international workshop on Link discovery, 36-43. doi:10.1145/1134271.1134277

Description

This network consists of books about US politics sold by Amazon.com. Ties represent books that are often purchased together, as revealed by Amazon's 'customers who bought this book also bought these other books' section on those books' pages on the website.

Information about the book's leaning "Liberal", "Neutral", or "Conservative" were added separately by Mark Newman based on the abstracts, descriptions, and reviews posted on Amazon.

These data should be cited as V. Krebs, unpublished, http://www.orgnet.com/.

Usage

data(irps_books)

Format

#> # A labelled, undirected network of 105 nodes and 441 ties
#> # A tibble: 105 x 2
#>   name                       Leaning     
#>   <chr>                      <chr>       
#> 1 1000 Years for Revenge     Neutral     
#> 2 Bush vs. the Beltway       Conservative
#> 3 Charlie Wilson's War       Conservative
#> 4 Losing Bin Laden           Conservative
#> 5 Sleeping With the Devil    Neutral     
#> 6 The Man Who Warned America Conservative
#> # i 99 more rows
#> # A tibble: 441 x 2
#>    from    to
#>   <int> <int>
#> 1     1     2
#> 2     1     3
#> 3     1     4
#> 4     2     4
#> 5     1     5
#> 6     3     5
#> # i 435 more rows

Author(s)

Valdis Krebs, Mark Newman

Description

This network is of contiguity between US states. States that share a border are connected by a tie in the network. The data is a network of 107 ties among 50 US states (nodes). States are named by their two-letter ISO-3166 code. This data includes also the names of the capitol cities of each state, which are listed in the node attribute 'capitol'.

Usage

data(irps_usgeo)

Format

#> # US State Contiguity
#> # A labelled, undirected network of 50 states and 107 contiguity ties
#> # A tibble: 50 x 3
#>   name  capitol     population
#>   <chr> <chr>            <int>
#> 1 AK    Juneau              NA
#> 2 AL    Montgomery     4780127
#> 3 AR    Little Rock    2915958
#> 4 AZ    Phoenix        6392307
#> 5 CA    Sacramento    37252895
#> 6 CO    Denver         5029324
#> # i 44 more rows
#> # A tibble: 107 x 2
#>    from    to
#>   <int> <int>
#> 1     2     9
#> 2     2    10
#> 3     2    25
#> 4     2    42
#> 5     3    18
#> 6     3    24
#> # i 101 more rows

References

Meghanathan, Natarajan. 2017. "Complex network analysis of the contiguous United States graph." Computer and Information Science, 10(1): 54-76. doi:10.5539/cis.v10n1p54

Description

This network records the evolution of the major relationship changes between the protagonists of World War I (WWI) from 1872 to 1907. It is incomplete both in terms of (eventual) parties to the war as well as some other relations, but gives a good overview of the main alliances and enmities.

The data series begins with the Three Emperors' League (1872, revived in 1881) between Germany, Austria-Hungary, and Russia. The Triple Alliance in 1882 joined Germany, Austria-Hungary, and Italy into a bloc that lasted until WWI. A bilateral alliance between Germany and Russia lapsed in 1890, and a French-Russian alliance developed between 1891-1894. The Entente Cordiale thawed and then fostered relations between Great Britain and France in 1904, and a British-Russian agreement in 1907 bound Great Britain, France, and Russia into the Triple Entente.

Usage

data(irps_wwi)

Format

#> # World War I Protagonists
#> # A dynamic, labelled, signed, undirected network of 6 European major powers
#> and 20 relationship ties
#> # A tibble: 6 x 1
#>   name 
#>   <chr>
#> 1 GBR  
#> 2 FRA  
#> 3 RUS  
#> 4 AUH  
#> 5 DEU  
#> 6 ITA  
#> # A tibble: 20 x 5
#>    from    to  sign begin   end
#>   <int> <int> <dbl> <dbl> <dbl>
#> 1     1     2    -1  1872  1904
#> 2     1     3    -1  1872  1907
#> 3     1     4    -1  1872  1918
#> 4     2     3    -1  1872  1890
#> 5     2     4    -1  1872  1918
#> 6     2     5    -1  1872  1918
#> # i 14 more rows

References

Antal, Tibor, Pavel Krapivsky, and Sidney Redner. 2006. "Social balance on networks: The dynamics of friendship and enmity". Physica D 224: 130-136. doi:10.1016/j.physd.2006.09.028

Description

One-mode subset of Coleman's adolescent society network (Coleman 1961), as used in Feld's (1991) "Why your friends have more friends than you do". Coleman collected data on friendships among students in 12 U.S. high schools. Feld explored a subset of 8 girls from one of these schools, "Marketville", and gave them fictitious names, which are retained here.

Usage

data(ison_adolescents)

Format

#> # The Adolescent Society
#> # A labelled, undirected network of 8 adolescents and 10 friendships ties
#> # A tibble: 8 x 1
#>   name 
#>   <chr>
#> 1 Betty
#> 2 Sue  
#> 3 Alice
#> 4 Jane 
#> 5 Dale 
#> 6 Pam  
#> # i 2 more rows
#> # A tibble: 10 x 2
#>    from    to
#>   <int> <int>
#> 1     1     2
#> 2     2     3
#> 3     3     4
#> 4     2     5
#> 5     3     5
#> 6     4     5
#> # i 4 more rows

References

Coleman, James S. 1961. The Adolescent Society. New York: Free Press.

Feld, Scott. 1991. “Why your friends have more friends than you do” American Journal of Sociology 96(6): 1464-1477. doi:10.1086/229693.

Description

Multiplex graph object of friends, social, and task ties between 16 anonymous students. M182 was an honors algebra class where researchers collected friendship, social, and task ties between 16 students. The edge attribute friends contains friendship ties, where 2 = best friends, 1 = friend, and 0 is not a friend. social consists of social interactions per hour, and tasks consists of task interactions per hour.

Usage

data(ison_algebra)

Format

#> # A multiplex, weighted, directed network of 16 nodes and 279 social, tasks,
#> and friends arcs
#> # A tibble: 279 x 4
#>    from    to type   weight
#>   <int> <int> <chr>   <dbl>
#> 1     1     5 social   1.2 
#> 2     1     5 tasks    0.3 
#> 3     1     8 social   0.15
#> 4     1     9 social   2.85
#> 5     1     9 tasks    0.3 
#> 6     1    10 social   6.45
#> # i 273 more rows

Source

See also data(studentnets.M182, package = "NetData") Larger comprehensive data set publicly available, contact Daniel A. McFarland for details.

References

McFarland, Daniel A. (2001) “Student Resistance.” American Journal of Sociology 107(3): 612-78. doi:10.1086/338779.

Description

This network should solely be used for demonstration purposes as it does not describe a real network. To convert into the two-mode version, assign ison_brandes %>% rename(type = twomode_type).

Usage

data(ison_brandes)

Format

#> # A undirected network of 11 nodes and 12 ties
#> # A tibble: 11 x 1
#>   twomode_type
#>   <lgl>       
#> 1 FALSE       
#> 2 FALSE       
#> 3 TRUE        
#> 4 FALSE       
#> 5 TRUE        
#> 6 TRUE        
#> # i 5 more rows
#> # A tibble: 12 x 2
#>    from    to
#>   <int> <int>
#> 1     1     3
#> 2     2     3
#> 3     3     4
#> 4     4     5
#> 5     4     6
#> 6     5     7
#> # i 6 more rows

Description

These data contain the frequent associations between the 62 dolphins of a pod of dolphins living off Doubtful Sound, New Zealand. Additional information can be found in the literature cited below.

Usage

data(ison_dolphins)

Format

#> # A labelled, undirected network of 62 nodes and 159 ties
#> # A tibble: 62 x 1
#>   name      
#>   <chr>     
#> 1 Beak      
#> 2 Beescratch
#> 3 Bumper    
#> 4 CCL       
#> 5 Cross     
#> 6 DN16      
#> # i 56 more rows
#> # A tibble: 159 x 2
#>    from    to
#>   <int> <int>
#> 1     4     9
#> 2     6    10
#> 3     7    10
#> 4     1    11
#> 5     3    11
#> 6     6    14
#> # i 153 more rows

References

Lusseau, David, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson. 2003. "The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations", Behavioral Ecology and Sociobiology 54, 396-405.

Lusseau, David. 2003. "The emergent properties of a dolphin social network", Proc. R. Soc. London B 270(S): S186-S188. doi:10.1098/rsbl.2003.0057

Lusseau, David. 2007. "Evidence for social role in a dolphin social network". Evolutionary Ecology 21: 357–366. doi:10.1007/s10682-006-9105-0

Description

21 managers of a company of just over 100 employees manufactured high-tech equipment on the west coast of the United States. Three types of ties were collected:

• friends: managers' answers to the question "Who is your friend?"

• advice: managers' answers to the question "To whom do you go to for advice?"

• reports: "To whom do you report?" based on company reports

The data is anonymised, but four nodal attributes are included:

• age: the manager's age in years

• tenure: the manager's length of service

• level: the manager's level in the corporate hierarchy, where 3 = CEO, 2 = Vice President, and 1 = manager

• dept: one of four departments, B, C, D, E, with the CEO alone in A

Usage

data(ison_hightech)

Format

#> # A multiplex, directed network of 21 nodes and 312 friends, advice, and
#> reports arcs
#> # A tibble: 21 x 4
#>     age tenure level dept 
#>   <dbl>  <dbl> <dbl> <chr>
#> 1    33      9     1 E    
#> 2    42     20     2 E    
#> 3    40     13     1 C    
#> 4    33      8     1 E    
#> 5    32      3     1 C    
#> 6    59     28     1 B    
#> # i 15 more rows
#> # A tibble: 312 x 3
#>    from    to type   
#>   <int> <int> <chr>  
#> 1     1     2 friends
#> 2     1     2 advice 
#> 3     1     2 reports
#> 4     1     4 friends
#> 5     1     4 advice 
#> 6     1     8 friends
#> # i 306 more rows

References

Krackhardt, David. 1987. "Cognitive social structures". Social Networks 9: 104-134.

Description

The network was observed in a university Karate club in 1977. The network describes association patterns among 34 members and maps out allegiance patterns between members and either Mr. Hi, the instructor, or the John A. the club president after an argument about hiking the price for lessons. The allegiance of each node is listed in the obc argument which takes the value 1 if the individual sided with Mr. Hi after the fight and 2 if the individual sided with John A.

Usage

data(ison_karateka)

Format

#> # A labelled, weighted, undirected network of 34 nodes and 78 ties
#> # A tibble: 34 x 2
#>   name  allegiance
#>   <chr>      <dbl>
#> 1 Mr Hi          1
#> 2 2              1
#> 3 3              1
#> 4 4              1
#> 5 5              1
#> 6 6              1
#> # i 28 more rows
#> # A tibble: 78 x 3
#>    from    to weight
#>   <int> <int>  <dbl>
#> 1     1     2      4
#> 2     1     3      5
#> 3     2     3      6
#> 4     1     4      3
#> 5     2     4      3
#> 6     3     4      3
#> # i 72 more rows

References

Zachary, Wayne W. 1977. “An Information Flow Model for Conflict and Fission in Small Groups.” Journal of Anthropological Research 33(4):452–73. doi:10.1086/jar.33.4.3629752.

Description

The Seven Bridges of Koenigsberg is a notable historical problem in mathematics and laid the foundations of graph theory. The city of Koenigsberg in Prussia (now Kaliningrad, Russia) was set on both sides of the Pregel River, and included two large islands which were connected to each other and the mainland by seven bridges. A weekend diversion for inhabitants was to find a walk through the city that would cross each bridge once and only once. The islands could not be reached by any route other than the bridges, and every bridge must have been crossed completely every time (one could not walk half way onto the bridge and then turn around and later cross the other half from the other side). In 1735, Leonard Euler proved that the problem has no solution.

Usage

data(ison_koenigsberg)

Format

#> # A labelled, undirected network of 4 nodes and 7 ties
#> # A tibble: 4 x 3
#>   name       lat   lon
#>   <chr>    <dbl> <dbl>
#> 1 Altstadt  54.7  20.5
#> 2 Kneiphof  54.7  20.5
#> 3 Lomse     54.7  20.5
#> 4 Vorstadt  54.7  20.5
#> # A tibble: 7 x 3
#>    from    to name           
#>   <int> <int> <chr>          
#> 1     1     2 Kraemer Bruecke
#> 2     1     2 Schmiedebruecke
#> 3     1     3 Holzbruecke    
#> 4     2     3 Honigbruecke   
#> 5     2     4 Gruene Bruecke 
#> 6     2     4 Koettelbruecke 
#> # i 1 more row

Source

{igraphdata}

References

Euler, Leonard. 1741. “Solutio problematis ad geometriam situs pertinentis.” Commentarii academiae scientiarum Petropolitanae.

Description

These networks are for demonstration purposes and do not describe any real world network. All examples contain named nodes. The networks are gathered together as a list and can be retrieved simply by plucking the desired network.

Usage

data(ison_laterals)

Format

#> $ison_bb
#> # A labelled, two-mode network of 10 nodes and 12 ties
#> # A tibble: 10 x 2
#>   name  type 
#>   <chr> <lgl>
#> 1 A     FALSE
#> 2 B     FALSE
#> 3 C     FALSE
#> 4 D     FALSE
#> 5 U     TRUE 
#> 6 V     TRUE 
#> # i 4 more rows
#> # A tibble: 12 x 2
#>    from    to
#>   <int> <int>
#> 1     1     5
#> 2     1     6
#> 3     2     5
#> 4     2     7
#> 5     2     8
#> 6     2     9
#> # i 6 more rows
#> 
#> $ison_bm
#> # A labelled, two-mode network of 8 nodes and 9 ties
#> # A tibble: 8 x 2
#>   name  type 
#>   <chr> <lgl>
#> 1 A     FALSE
#> 2 B     FALSE
#> 3 C     FALSE
#> 4 D     FALSE
#> 5 U     TRUE 
#> 6 V     TRUE 
#> # i 2 more rows
#> # A tibble: 9 x 2
#>    from    to
#>   <int> <int>
#> 1     1     5
#> 2     1     6
#> 3     2     5
#> 4     2     7
#> 5     2     8
#> 6     3     6
#> # i 3 more rows
#> 
#> $ison_mb
#> # A labelled, two-mode network of 8 nodes and 9 ties
#> # A tibble: 8 x 2
#>   name  type 
#>   <chr> <lgl>
#> 1 A     FALSE
#> 2 B     FALSE
#> 3 C     FALSE
#> 4 D     FALSE
#> 5 M     TRUE 
#> 6 X     TRUE 
#> # i 2 more rows
#> # A tibble: 9 x 2
#>    from    to
#>   <int> <int>
#> 1     1     5
#> 2     2     5
#> 3     2     6
#> 4     2     7
#> 5     3     5
#> 6     3     6
#> # i 3 more rows
#> 
#> $ison_mm
#> # A labelled, two-mode network of 6 nodes and 6 ties
#> # A tibble: 6 x 2
#>   name  type 
#>   <chr> <lgl>
#> 1 A     FALSE
#> 2 B     FALSE
#> 3 C     FALSE
#> 4 D     FALSE
#> 5 M     TRUE 
#> 6 N     TRUE 
#> # A tibble: 6 x 2
#>    from    to
#>   <int> <int>
#> 1     1     5
#> 2     2     5
#> 3     2     6
#> 4     3     5
#> 5     3     6
#> 6     4     6

Description

One-mode network dataset collected by Lazega (2001) on the relations between partners in a corporate law firm called SG&R in New England 1988-1991. This particular subset includes the 36 partners among the 71 attorneys of this firm. Nodal attributes include seniority, formal status, office in which they work, gender, lawschool they attended, their age, and how many years they had been at the firm.

Usage

data(ison_lawfirm)

Format

#> # A multiplex, directed network of 71 nodes and 2571 friends, advice, and
#> cowork arcs
#> # A tibble: 71 x 7
#>   status  gender office   seniority   age practice   school      
#>   <chr>   <chr>  <chr>        <dbl> <dbl> <chr>      <chr>       
#> 1 partner man    Boston          31    64 litigation Harvard/Yale
#> 2 partner man    Boston          32    62 corporate  Harvard/Yale
#> 3 partner man    Hartford        13    67 litigation Harvard/Yale
#> 4 partner man    Boston          31    59 corporate  Other       
#> 5 partner man    Hartford        31    59 litigation UConn       
#> 6 partner man    Hartford        29    55 litigation Harvard/Yale
#> # i 65 more rows
#> # A tibble: 2,571 x 3
#>    from    to type   
#>   <int> <int> <chr>  
#> 1     1     2 friends
#> 2     1     2 advice 
#> 3     1     4 friends
#> 4     1     8 friends
#> 5     1    17 friends
#> 6     1    17 advice 
#> # i 2,565 more rows

Details

The larger data from which this subset comes includes also individual performance measurements (hours worked, fees brought in) and attitudes concerning various management policy options (see also {sand}), their strong-coworker network, advice network, friendship network, and indirect control network.

Source

{networkdata}

References

Lazega, Emmanuel. 2001. The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership. Oxford: Oxford University Press.

Description

This package includes two datasets related to the Marvel comic book universe. The first, ison_marvel_teams, is a two-mode affiliation network of 53 Marvel comic book characters and their affiliations to 141 different teams. This network includes only information about nodes' names and nodeset, but additional nodal data can be taken from the other Marvel dataset here.

The second network, ison_marvel_relationships, is a one-mode signed network of friendships and enmities between the 53 Marvel comic book characters. Friendships are indicated by a positive sign in the tie sign attribute, whereas enmities are indicated by a negative sign in this edge attribute.

Usage

data(ison_marvel_teams)

data(ison_marvel_relationships)

Format

#> # A labelled, two-mode network of 194 nodes and 683 ties
#> # A tibble: 194 x 2
#>   type  name         
#>   <lgl> <chr>        
#> 1 FALSE Abomination  
#> 2 FALSE Ant-Man      
#> 3 FALSE Apocalypse   
#> 4 FALSE Beast        
#> 5 FALSE Black Panther
#> 6 FALSE Black Widow  
#> # i 188 more rows
#> # A tibble: 683 x 2
#>    from    to
#>   <int> <int>
#> 1     1   120
#> 2     1   152
#> 3     1   160
#> 4     1   162
#> 5     1   179
#> 6     2    56
#> # i 677 more rows

#> # A labelled, complex, signed, undirected network of 53 nodes and 558 ties
#> # A tibble: 53 x 10
#>   name     Gender Appearances Attractive  Rich Intellect Omnilingual PowerOrigin
#>   <chr>    <chr>        <int>      <int> <int>     <int>       <int> <chr>      
#> 1 Abomina~ Male           427          0     0         1           1 Radiation  
#> 2 Ant-Man  Male           589          1     0         1           0 Human      
#> 3 Apocaly~ Male          1207          0     0         1           1 Mutant     
#> 4 Beast    Male          7609          1     0         1           0 Mutant     
#> 5 Black P~ Male          2189          1     1         1           0 Human      
#> 6 Black W~ Female        2907          1     0         1           0 Human      
#> # i 47 more rows
#> # i 2 more variables: UnarmedCombat <int>, ArmedCombat <int>
#> # A tibble: 558 x 3
#>    from    to  sign
#>   <int> <int> <dbl>
#> 1     1     4    -1
#> 2     1    11    -1
#> 3     1    12    -1
#> 4     1    23    -1
#> 5     1    24    -1
#> 6     1    25    -1
#> # i 552 more rows

Details

Additional nodal variables have been coded and included by Dr Umut Yuksel:

• Gender: binary character, 43 "Male" and 10 "Female"

• PowerOrigin: binary character, 2 "Alien", 1 "Cyborg", 5 "God/Eternal", 22 "Human", 1 "Infection", 16 "Mutant", 5 "Radiation", 1 "Robot"

• Appearances: integer, in how many comic book issues they appeared in

• Attractive: binary integer, 41 1 (yes) and 12 0 (no)

• Rich: binary integer, 11 1 (yes) and 42 0 (no)

• Intellect: binary integer, 39 1 (yes) and 14 0 (no)

• Omnilingual: binary integer, 8 1 (yes) and 45 0 (no)

• UnarmedCombat: binary integer, 51 1 (yes) and 2 0 (no)

• ArmedCombat: binary integer, 25 1 (yes) and 28 0 (no)

Source

Umut Yuksel, 31 March 2017

Description

The data were collected for an ethnographic study of community structure in a New England monastery. Various sociometric data was collected of the novices attending the minor seminary of 'Cloisterville' preparing to join the monastic order.

• type = "like" records whom novices said they liked most at three time points/waves

• type = "esteem" records whom novices said they held in esteem (sign > 0) and disesteem (sign < 0)

• type = "praise" records whom novices said they praised (sign > 0) and blamed (sign < 0)

• type = "influence" records whom novices said were a positive influence (sign > 0) and negative influence (sign < 0)

All networks are weighted. Novices' first choices are weighted 3, the second 2, and third choices 1. Some subjects offered tied ranks for their top four choices.

In addition to node names, a 'groups' variable records the four groups that Sampson observed during his time there:

• The Loyal Opposition consists of novices who entered the monastery first and defended existing practices

• The Young Turks arrived later during a period of change and questioned practices in the monastery

• The Interstitial did not take sides in the debate

• The Outcasts were novices that were not accepted in the group

Information about senior monks was not included. While type = "like" is observed over three waves, the rest of the data was recorded retrospectively from the end of the study, after the network fragmented. The waves in which the novitiates were expelled (1), voluntarily departed (2 and 3), or remained (4) are given in the nodal attribute "left".

Usage

data(ison_monks)

Format

#> # A longitudinal, labelled, multiplex, signed, weighted, directed network of 18
#> nodes and 463 like, esteem, influence, and praise arcs
#> # A tibble: 18 x 3
#>   name        groups        left
#>   <chr>       <chr>        <dbl>
#> 1 Romuald     Interstitial     3
#> 2 Bonaventure Loyal            4
#> 3 Ambrose     Loyal            4
#> 4 Berthold    Loyal            4
#> 5 Peter       Loyal            3
#> 6 Louis       Loyal            4
#> # i 12 more rows
#> # A tibble: 463 x 6
#>    from    to  sign type  weight  wave
#>   <int> <int> <dbl> <chr>  <dbl> <dbl>
#> 1     1     2     1 like       1     2
#> 2     1     2     1 like       1     3
#> 3     1     3     1 like       1     3
#> 4     1     5     1 like       3     1
#> 5     1     5     1 like       3     2
#> 6     1     5     1 like       3     3
#> # i 457 more rows

References

Sampson, Samuel F. 1969. Crisis in a cloister. Unpublished doctoral dissertation, Cornell University.

Breiger R., Boorman S. and Arabie P. 1975. "An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling". Journal of Mathematical Psychology, 12: 328-383.

Description

A directed, simple, named, weighted graph with 32 nodes and 440 edges. Nodes are academics and edges illustrate the communication patterns on an Electronic Information Exchange System among them. Node attributes include the number of citations (Citations) and the discipline of the researchers (Discipline). Edge weights illustrate the number of emails sent from one academic to another over the studied time period.

Usage

data(ison_networkers)

Format

#> # A labelled, weighted, directed network of 32 nodes and 440 arcs
#> # A tibble: 32 x 3
#>   name               Discipline   Citations
#>   <chr>              <chr>            <dbl>
#> 1 Lin Freeman        Sociology           19
#> 2 Doug White         Anthropology         3
#> 3 Ev Rogers          Other              170
#> 4 Richard Alba       Sociology           23
#> 5 Phipps Arabie      Other               16
#> 6 Carol Barner-Barry Other                6
#> # i 26 more rows
#> # A tibble: 440 x 3
#>    from    to weight
#>   <int> <int>  <dbl>
#> 1     1     2    488
#> 2     1     3     28
#> 3     1     4     65
#> 4     1     5     20
#> 5     1     6     65
#> 6     1     7     45
#> # i 434 more rows

Source

networkdata package

References

Freeman, Sue C. and Linton C. Freeman. 1979. The networkers network: A study of the impact of a new communications medium on sociometric structure. Social Science Research Reports No 46. Irvine CA, University of California.

Wasserman Stanley and Katherine Faust. 1994. Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge.

Description

Ron Burt prepared this data from Coleman, Katz and Menzel's 1966 study on medical innovation. They had collected data from physicians in four towns in Illinois: Peoria, Bloomington, Quincy and Galesburg. These four networks are held as separate networks in a list.

Coleman, Katz and Menzel were concerned with the impact of network ties on the physicians' adoption of a new drug, tetracycline. Data on three types of ties were collected in response to three questions:

• advice: "When you need information or advice about questions of therapy where do you usually turn?"

• discussion: "And who are the three or four physicians with whom you most often find yourself discussing cases or therapy in the course of an ordinary week – last week for instance?"

• friendship: "Would you tell me the first names of your three friends whom you see most often socially?"

Additional questions and records of prescriptions provided additional information:

• recorded date of tetracycline adoption date

• years in practice (note that these are {messydates}-compatible dates)

• conferences attended (those that attended "Specialty" conferences presumably also attended "General" conferences)

• regular subscriptions to medical journals

• free_time spent associating with doctors

• discussions on medical matters when with other doctors sociallyy

• memberships in clubs with other doctores

• number of top 3 friends that are doctors

• time practicing in current community

• patients load (ordinal)

• physical proximity to other physicians (in building/sharing office)

• medical specialty (GP/Internist/Pediatrician/Other)

Usage

data(ison_physicians)

Format

#> $Peoria
#> # A multiplex, directed network of 117 nodes and 543 friendship, advice, and
#> discussion arcs
#> # A tibble: 117 x 12
#>   adoption specialty    conferences journals practice   community patients
#>      <dbl> <chr>        <chr>          <dbl> <chr>      <chr>     <chr>   
#> 1        1 Pediatrician Specialty          9 1920..1929 20+yrs    101-150 
#> 2       12 GP           None               5 1945..     -1yr      76-100  
#> 3        8 Internist    General            7 1935..1939 10-20yrs  76-100  
#> 4        9 GP           General            6 1940..1944 5-10yrs   51-75   
#> 5        9 GP           General            4 1935..1939 10-20yrs  51-75   
#> 6       10 Internist    None               7 1930..1934 10-20yrs  101-150 
#> # i 111 more rows
#> # i 5 more variables: doc_freetime <dbl>, doc_discuss <dbl>, doc_friends <dbl>,
#> #   doc_club <dbl>, doc_proximity <chr>
#> # A tibble: 543 x 3
#>    from    to type      
#>   <int> <int> <chr>     
#> 1     1     8 friendship
#> 2     1    58 friendship
#> 3     1    87 advice    
#> 4     1    90 advice    
#> 5     1   110 advice    
#> 6     1   112 friendship
#> # i 537 more rows
#> 
#> $Bloomington
#> # A multiplex, directed network of 50 nodes and 211 friendship, discussion, and
#> advice arcs
#> # A tibble: 50 x 12
#>   adoption specialty conferences journals practice   community patients
#>      <dbl> <chr>     <chr>          <dbl> <chr>      <chr>     <chr>   
#> 1       98 Internist Specialty          8 1930..1934 10-20yrs  101-150 
#> 2        1 GP        General            3 1945..     5-10yrs   76-100  
#> 3       98 GP        Specialty          4 1930..1934 10-20yrs  101-150 
#> 4        7 Internist None               3 1945..     -1yr      26-50   
#> 5        6 Internist General            9 1935..1939 5-10yrs   76-100  
#> 6        1 GP        Specialty          5 1935..1939 10-20yrs  101-150 
#> # i 44 more rows
#> # i 5 more variables: doc_freetime <dbl>, doc_discuss <dbl>, doc_friends <dbl>,
#> #   doc_club <dbl>, doc_proximity <chr>
#> # A tibble: 211 x 3
#>    from    to type      
#>   <int> <int> <chr>     
#> 1     1     3 friendship
#> 2     1    10 discussion
#> 3     1    24 advice    
#> 4     1    44 advice    
#> 5     2     4 advice    
#> 6     2     6 advice    
#> # i 205 more rows
#> 
#> $Quincy
#> # A multiplex, directed network of 44 nodes and 174 advice, discussion, and
#> friendship arcs
#> # A tibble: 44 x 12
#>   adoption specialty conferences journals practice   community patients
#>      <dbl> <chr>     <chr>          <dbl> <chr>      <chr>     <chr>   
#> 1        2 Internist None               6 1935..1939 10-20yrs  151+    
#> 2       18 GP        General            3 1920..1929 20+yrs    151+    
#> 3       18 Internist None               5 1945..     -1yr      -25     
#> 4        4 GP        General            3 1930..1934 20+yrs    151+    
#> 5       18 GP        Specialty          4 1935..1939 10-20yrs  151+    
#> 6        5 Internist General            5 ..1919     20+yrs    51-75   
#> # i 38 more rows
#> # i 5 more variables: doc_freetime <dbl>, doc_discuss <dbl>, doc_friends <dbl>,
#> #   doc_club <dbl>, doc_proximity <chr>
#> # A tibble: 174 x 3
#>    from    to type      
#>   <int> <int> <chr>     
#> 1     1     8 advice    
#> 2     1     9 advice    
#> 3     1    10 discussion
#> 4     1    13 friendship
#> 5     1    15 advice    
#> 6     1    22 discussion
#> # i 168 more rows
#> 
#> $Galesburg
#> # A multiplex, directed network of 35 nodes and 171 advice, discussion, and
#> friendship arcs
#> # A tibble: 35 x 12
#>   adoption specialty conferences journals practice   community patients
#>      <dbl> <chr>     <chr>          <dbl> <chr>      <chr>     <chr>   
#> 1       18 GP        General            4 1935..1939 5-10yrs   101-150 
#> 2       18 GP        None               4 1935..1939 -1yr      151+    
#> 3        4 GP        General            6 1945..     2-5yrs    51-75   
#> 4        5 GP        None               4 1935..1939 10-20yrs  101-150 
#> 5        8 Internist General            6 1935..1939 5-10yrs   151+    
#> 6        4 Internist Specialty          8 ..1919     20+yrs    76-100  
#> # i 29 more rows
#> # i 5 more variables: doc_freetime <dbl>, doc_discuss <dbl>, doc_friends <dbl>,
#> #   doc_club <dbl>, doc_proximity <chr>
#> # A tibble: 171 x 3
#>    from    to type      
#>   <int> <int> <chr>     
#> 1     1     5 advice    
#> 2     1     6 advice    
#> 3     1    20 discussion
#> 4     1    23 discussion
#> 5     1    30 friendship
#> 6     1    31 friendship
#> # i 165 more rows

Source

{networkdata}

References

Coleman, James, Elihu Katz, and Herbert Menzel. 1966. Medical innovation: A diffusion study. Indianapolis: The Bobbs-Merrill Company.

Description

Two-mode network dataset collected by Davis, Gardner and Gardner (1941) about the pattern of a group of women's participation at informal social events in Old City during a 9 month period, as reported in the Old City Herald in 1936. By convention, the nodes are named by the women's first names and the code numbers of the events, but the women's surnames and titles (Miss, Mrs.) are recorded here too. The events' dates are recorded in place of the Surname, and these dates are also offered as a tie attribute.

Usage

data(ison_southern_women)

Format

#> # Southern Women Data
#> # A labelled, two-mode network of 18 women and 14 social events and 89
#> participation ties
#> # A tibble: 32 x 4
#>   type  name      Surname    Title
#>   <lgl> <chr>     <chr>      <chr>
#> 1 FALSE Evelyn    Jefferson  Mrs  
#> 2 FALSE Laura     Mandeville Miss 
#> 3 FALSE Theresa   Anderson   Miss 
#> 4 FALSE Brenda    Rogers     Miss 
#> 5 FALSE Charlotte McDowd     Miss 
#> 6 FALSE Frances   Anderson   Miss 
#> # i 26 more rows
#> # A tibble: 89 x 3
#>    from    to date      
#>   <int> <int> <date>    
#> 1    14    29 1936-02-23
#> 2    15    29 1936-02-23
#> 3    17    29 1936-02-23
#> 4    18    29 1936-02-23
#> 5     1    23 1936-02-25
#> 6     2    23 1936-02-25
#> # i 83 more rows

References

Davis, Allison, Burleigh B. Gardner, and Mary R. Gardner. 1941. Deep South. Chicago: University of Chicago Press.

Description

One-mode network dataset collected by Gabasova (2016) on the interactions between Star Wars characters in each movie from Episode 1 (The Phantom Menace) to Episode 7 (The Force Awakens). There is a separate network for each episode, and the data is listed in order from episode 1 to 7. The network for each episode varies in the number of nodes and ties. For all networks, characters are named (eg. R2-D2, Anakin, Chewbacca) and the following node attributes are provided where available: height, mass, hair color, skin color, eye color, birth year, sex, homeworld, and species. The node attribute 'faction' has also been added, denoting the faction (eg. Jedi, Rebel Alliance, etc) that Star Wars characters belong to in each episode (coding completed with help of Yichen Shen and Tiphaine Aeby). Weighted ties represent the number of times characters speak within the same scene of the film.

Usage

data(ison_starwars)

Format

#> $`Episode I`
#> # A labelled, weighted, undirected network of 38 nodes and 135 ties
#> # A tibble: 38 x 11
#>   name   height  mass hair_color skin_color eye_color birth_year sex   homeworld
#>   <chr>   <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>    
#> 1 R2-D2      96    32 <NA>       white, bl~ red               33 none  Naboo    
#> 2 QUI-G~    193    89 brown      fair       blue              92 male  <NA>     
#> 3 NUTE ~    191    90 none       mottled g~ red               NA male  Cato Nei~
#> 4 PK-4       NA    NA <NA>       <NA>       <NA>              NA <NA>  <NA>     
#> 5 TC-14      NA    NA <NA>       <NA>       <NA>              NA <NA>  <NA>     
#> 6 OBI-W~    182    77 auburn, w~ fair       blue-gray         57 male  Stewjon  
#> # i 32 more rows
#> # i 2 more variables: species <chr>, faction <chr>
#> # A tibble: 135 x 3
#>    from    to weight
#>   <int> <int>  <int>
#> 1     1    16     11
#> 2     1     2     14
#> 3     1    19     16
#> 4     1    18      3
#> 5     1    23      2
#> 6     1    25      2
#> # i 129 more rows
#> 
#> $`Episode II`
#> # A labelled, weighted, undirected network of 33 nodes and 101 ties
#> # A tibble: 33 x 11
#>   name   height  mass hair_color skin_color eye_color birth_year sex   homeworld
#>   <chr>   <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>    
#> 1 R2-D2      96    32 <NA>       white, bl~ red               33 none  Naboo    
#> 2 CAPTA~    185    85 black      dark       brown             NA male  Naboo    
#> 3 EMPER~    170    75 grey       pale       yellow            82 male  Naboo    
#> 4 SENAT~     NA    NA <NA>       <NA>       <NA>              NA <NA>  <NA>     
#> 5 ORN F~     NA    NA <NA>       <NA>       <NA>              NA <NA>  <NA>     
#> 6 MACE ~    188    84 none       dark       brown             72 male  Haruun K~
#> # i 27 more rows
#> # i 2 more variables: species <chr>, faction <chr>
#> # A tibble: 101 x 3
#>    from    to weight
#>   <int> <int>  <int>
#> 1     1    13      7
#> 2     1    12      7
#> 3     1    24      3
#> 4     3     4      2
#> 5     3     5      2
#> 6     4     5      1
#> # i 95 more rows
#> 
#> $`Episode III`
#> # A labelled, weighted, undirected network of 24 nodes and 65 ties
#> # A tibble: 24 x 11
#>   name   height  mass hair_color skin_color eye_color birth_year sex   homeworld
#>   <chr>   <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>    
#> 1 R2-D2      96    32 <NA>       white, bl~ red             33   none  Naboo    
#> 2 ANAKIN    188    84 blond      fair       blue            41.9 male  Tatooine 
#> 3 OBI-W~    182    77 auburn, w~ fair       blue-gray       57   male  Stewjon  
#> 4 ODD B~     NA    NA <NA>       <NA>       <NA>            NA   <NA>  <NA>     
#> 5 GENER~    216   159 none       brown, wh~ green, y~       NA   male  Kalee    
#> 6 EMPER~    170    75 grey       pale       yellow          82   male  Naboo    
#> # i 18 more rows
#> # i 2 more variables: species <chr>, faction <chr>
#> # A tibble: 65 x 3
#>    from    to weight
#>   <int> <int>  <int>
#> 1     1     6      2
#> 2     1     3     12
#> 3     1     2      9
#> 4     1     9      5
#> 5     1     8      4
#> 6     1    10      4
#> # i 59 more rows
#> 
#> $`Episode IV`
#> # A labelled, weighted, undirected network of 21 nodes and 60 ties
#> # A tibble: 21 x 11
#>   name   height  mass hair_color skin_color eye_color birth_year sex   homeworld
#>   <chr>   <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>    
#> 1 R2-D2      96    32 <NA>       white, bl~ red             33   none  Naboo    
#> 2 CHEWB~    228   112 brown      unknown    blue           200   male  Kashyyyk 
#> 3 C-3PO     167    75 <NA>       gold       yellow         112   none  Tatooine 
#> 4 LUKE      172    77 blond      fair       blue            19   male  Tatooine 
#> 5 DARTH~    202   136 none       white      yellow          41.9 male  Tatooine 
#> 6 CAMIE      NA    NA <NA>       <NA>       <NA>            NA   <NA>  <NA>     
#> # i 15 more rows
#> # i 2 more variables: species <chr>, faction <chr>
#> # A tibble: 60 x 3
#>    from    to weight
#>   <int> <int>  <int>
#> 1     1     2      3
#> 2     1     3     17
#> 3     1     9      1
#> 4     1     4     14
#> 5     1    10      1
#> 6     1    11      4
#> # i 54 more rows
#> 
#> $`Episode V`
#> # A labelled, weighted, undirected network of 21 nodes and 55 ties
#> # A tibble: 21 x 11
#>   name   height  mass hair_color skin_color eye_color birth_year sex   homeworld
#>   <chr>   <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>    
#> 1 R2-D2      96    32 <NA>       white, bl~ red               33 none  Naboo    
#> 2 CHEWB~    228   112 brown      unknown    blue             200 male  Kashyyyk 
#> 3 LUKE      172    77 blond      fair       blue              19 male  Tatooine 
#> 4 HAN       180    80 brown      fair       brown             29 male  Corellia 
#> 5 RIEEK~     NA    NA <NA>       <NA>       <NA>              NA <NA>  <NA>     
#> 6 LEIA      150    49 brown      light      brown             19 fema~ Alderaan 
#> # i 15 more rows
#> # i 2 more variables: species <chr>, faction <chr>
#> # A tibble: 55 x 3
#>    from    to weight
#>   <int> <int>  <int>
#> 1     1     2      5
#> 2     1     7     10
#> 3     1     3      7
#> 4     1     4      4
#> 5     1     6      5
#> 6     1    21      1
#> # i 49 more rows
#> 
#> $`Episode VI`
#> # A labelled, weighted, undirected network of 20 nodes and 55 ties
#> # A tibble: 20 x 11
#>   name   height  mass hair_color skin_color eye_color birth_year sex   homeworld
#>   <chr>   <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>    
#> 1 R2-D2      96    32 <NA>       white, bl~ red             33   none  Naboo    
#> 2 CHEWB~    228   112 brown      unknown    blue           200   male  Kashyyyk 
#> 3 JERJE~     NA    NA <NA>       <NA>       <NA>            NA   <NA>  <NA>     
#> 4 DARTH~    202   136 none       white      yellow          41.9 male  Tatooine 
#> 5 C-3PO     167    75 <NA>       gold       yellow         112   none  Tatooine 
#> 6 BIB F~    180    NA none       pale       pink            NA   male  Ryloth   
#> # i 14 more rows
#> # i 2 more variables: species <chr>, faction <chr>
#> # A tibble: 55 x 3
#>    from    to weight
#>   <int> <int>  <int>
#> 1     1     2      8
#> 2     1     5     14
#> 3     1    12      2
#> 4     1     7      2
#> 5     1     8      8
#> 6     1    10      9
#> # i 49 more rows
#> 
#> $`Episode VII`
#> # A labelled, weighted, undirected network of 27 nodes and 92 ties
#> # A tibble: 27 x 11
#>   name   height  mass hair_color skin_color eye_color birth_year sex   homeworld
#>   <chr>   <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>    
#> 1 LUKE      172    77 blond      fair       blue              19 male  Tatooine 
#> 2 R2-D2      96    32 <NA>       white, bl~ red               33 none  Naboo    
#> 3 CHEWB~    228   112 brown      unknown    blue             200 male  Kashyyyk 
#> 4 BB-8       NA    NA none       none       black             NA none  <NA>     
#> 5 LOR S~     NA    NA <NA>       <NA>       <NA>              NA <NA>  <NA>     
#> 6 POE        NA    NA brown      light      brown             NA male  <NA>     
#> # i 21 more rows
#> # i 2 more variables: species <chr>, faction <chr>
#> # A tibble: 92 x 3
#>    from    to weight
#>   <int> <int>  <int>
#> 1     2     3      1
#> 2     2     4      2
#> 3     3     4      9
#> 4     2    18      2
#> 5     3     9     20
#> 6     3    11     13
#> # i 86 more rows

Details

The network for each episode may be extracted and used separately, eg. ison_starwars[[1]] or ⁠ison_starwars$Episode I⁠ for Episode 1.

References

Gabasova, E. (2016). Star Wars social network.. doi:10.5281/zenodo.1411479

Description

Researchers regularly need to work with a variety of external data formats. The following functions offer ways to import from some common external file formats into objects that {manynet} and other graph/network packages in R can work with:

• read_matrix() imports adjacency matrices from Excel/csv files.

• read_edgelist() imports edgelists from Excel/csv files.

• read_nodelist() imports nodelists from Excel/csv files.

• read_pajek() imports Pajek (.net or .paj) files.

• read_ucinet() imports UCINET files from the header (.##h).

• read_dynetml() imports DyNetML interchange format for rich social network data.

• read_graphml() imports GraphML files.

Usage

read_cran(pkg = "all")

Arguments

Details

Note that these functions are not as actively maintained as others in the package, so please let us know if any are not currently working for you or if there are missing import routines by raising an issue on Github.

Source

https://www.r-bloggers.com/2016/01/r-graph-objects-igraph-vs-network/

See Also

as

Other makes: make_create, make_ego, make_explicit, make_learning, make_motifs, make_play, make_random, make_read, make_stochastic, make_write

Examples

# mnet <- read_cran()
# mnet <- to_ego(mnet, "manynet", max_dist = 2)
# graphr(mnet, layout = "hierarchy", 
#        edge_color = "type", node_color = "Compilation")

Description

These functions create networks with particular structural properties.

• create_empty() creates an empty network without any ties.

• create_filled() creates a filled network with every possible tie realised.

• create_ring() creates a ring or chord network where each nodes' neighbours form a clique.

• create_star() creates a network with a maximally central node.

• create_tree() creates a network with successive branches.

• create_lattice() creates a network that forms a regular tiling.

• create_components() creates a network that clusters nodes into separate components.

• create_core() creates a network in which a certain proportion of 'core' nodes are densely tied to each other, and the rest peripheral, tied only to the core.

• create_degree() creates a network with a given (out/in)degree sequence, which can also be used to create k-regular networks.

These functions can create either one-mode or two-mode networks. To create a one-mode network, pass the main argument n a single integer, indicating the number of nodes in the network. To create a two-mode network, pass n a vector of two integers, where the first integer indicates the number of nodes in the first mode, and the second integer indicates the number of nodes in the second mode. As an alternative, an existing network can be provided to n and the number of modes, nodes, and directedness will be inferred.

Usage

create_empty(n, directed = FALSE)

create_filled(n, directed = FALSE)

create_ring(n, directed = FALSE, width = 1, ...)

create_star(n, directed = FALSE)

create_tree(n, directed = FALSE, width = 2)

create_lattice(n, directed = FALSE, width = 8)

create_components(n, directed = FALSE, membership = NULL)

create_degree(n, outdegree = NULL, indegree = NULL)

create_core(n, directed = FALSE, mark = NULL)

Arguments

Value

By default a tbl_graph object is returned, but this can be coerced into other types of objects using as_edgelist(), as_matrix(), as_tidygraph(), or as_network().

By default, all networks are created as undirected. This can be overruled with the argument directed = TRUE. This will return a directed network in which the arcs are out-facing or equivalent. This direction can be swapped using to_redirected(). In two-mode networks, the directed argument is ignored.

Lattice graphs

create_lattice() creates both two-dimensional grid and triangular lattices with as even dimensions as possible. When the width parameter is set to 4, nodes cannot have (in or out) degrees larger than 4. This creates regular square grid lattices where possible. Such a network is bipartite, that is partitionable into two types that are not adjacent to any of their own type. If the number of nodes is a prime number, it will only return a chain (a single dimensional lattice).

A width parameter of 8 creates a network where the maximum degree of any nodes is 8. This can create a triangular mesh lattice or a Queen's move lattice, depending on the dimensions. A width parameter of 12 creates a network where the maximum degree of any nodes is 12. Prime numbers of nodes will return a chain.

See Also

as

Other makes: make_cran, make_ego, make_explicit, make_learning, make_motifs, make_play, make_random, make_read, make_stochastic, make_write

Examples

create_empty(10)
create_filled(10)
create_ring(8, width = 2)
create_star(12)
create_tree(c(7,8))
create_lattice(12, width = 4)
create_components(10, membership = c(1,1,1,2,2,2,3,3,3,3))
create_degree(10, outdegree = rep(1:5, 2))
create_core(6)

Description

This function creates an ego network through interactive interview questions. Note that it only creates a simplex, directed network.

Usage

create_ego(
  max_alters = Inf,
  roster = NULL,
  interpreter = FALSE,
  interrelater = FALSE
)

Arguments

See Also

Other makes: make_cran, make_create, make_explicit, make_learning, make_motifs, make_play, make_random, make_read, make_stochastic, make_write

Description

This function creates a network from a vector of explicitly named nodes and ties between them. create_explicit() largely wraps igraph::graph_from_literal(), but will also accept character input and not just a formula, and will never simplify the result.

Ties are indicated by -, and directed ties (arcs) require + at either or both ends. Ties are separated by commas, and isolates can be added as an additional, unlinked node after the comma within the formula. Sets of nodes can be linked to other sets of nodes through use of a semi-colon. See the example for a demonstration.

Usage

create_explicit(...)

Arguments

See Also

Other makes: make_cran, make_create, make_ego, make_learning, make_motifs, make_play, make_random, make_read, make_stochastic, make_write

Examples

create_explicit(A -+ B, B -+ C, A +-+ C, D, E:F:G-+A, E:F+-+G:H)

Description

These functions allow learning games to be played upon networks.

• play_learning() plays a DeGroot learning model upon a network.

• play_segregation() plays a Schelling segregation model upon a network.

Usage

play_learning(.data, beliefs, steps, epsilon = 5e-04)

play_segregation(
  .data,
  attribute,
  heterophily = 0,
  who_moves = c("ordered", "random", "most_dissatisfied"),
  choice_function = c("satisficing", "optimising", "minimising"),
  steps
)

Arguments

See Also

Other makes: make_cran, make_create, make_ego, make_explicit, make_motifs, make_play, make_random, make_read, make_stochastic, make_write

Other models: make_play

Examples

play_learning(ison_networkers, 
      rbinom(net_nodes(ison_networkers),1,prob = 0.25))
  startValues <- rbinom(100,1,prob = 0.5)
  startValues[sample(seq_len(100), round(100*0.2))] <- NA
  latticeEg <- create_lattice(100)
  latticeEg <- add_node_attribute(latticeEg, "startValues", startValues)
  latticeEg
  play_segregation(latticeEg, "startValues", 0.5)
  # graphr(latticeEg, node_color = "startValues", node_size = 5) + 
  # graphr(play_segregation(latticeEg, "startValues", 0.2), 
  #            node_color = "startValues", node_size = 5)

Description

create_motifs() is used to create a list of networks that represent the subgraphs or motifs corresponding to a certain number of nodes and direction. Note that currently only n==2 to n==4 is implemented, and the latter only for undirected networks.

Usage

create_motifs(n, directed = FALSE)

Arguments

See Also

Other makes: make_cran, make_create, make_ego, make_explicit, make_learning, make_play, make_random, make_read, make_stochastic, make_write

Description

These functions simulate diffusion or compartment models upon a network.

• play_diffusion() runs a single simulation of a compartment model, allowing the results to be visualised and examined.

• play_diffusions() runs multiple simulations of a compartment model for more robust inference.

These functions allow both a full range of compartment models, as well as simplex and complex diffusion to be simulated upon a network.

Usage

play_diffusion(
  .data,
  seeds = 1,
  contact = NULL,
  prevalence = 0,
  thresholds = 1,
  transmissibility = 1,
  latency = 0,
  recovery = 0,
  waning = 0,
  immune = NULL,
  steps
)

play_diffusions(
  .data,
  seeds = 1,
  contact = NULL,
  prevalence = 0,
  thresholds = 1,
  transmissibility = 1,
  latency = 0,
  recovery = 0,
  waning = 0,
  immune = NULL,
  steps,
  times = 5,
  strategy = "sequential",
  verbose = FALSE
)

Arguments

Simple and complex diffusion

By default, the function will simulate a simple diffusion process in which some infectious disease or idea diffuses from seeds through contacts at some constant rate (transmissibility).

These seeds can be specified by a vector index (the number of the position of each node in the network that should serve as a seed) or as a logical vector where TRUE is interpreted as already infected.

thresholds can be set such that adoption/infection requires more than one (the default) contact already being infected. This parameter also accepts a vector so that thresholds can vary.

Complex diffusion is where the thresholds are defined less than one. In this case, the thresholds are interpreted as proportional. That is, the threshold to adoption/infection is defined by the proportion of the node's contacts infected.

Nodes that cannot be infected can be indicated as immune with a logical vector or index, similar to how seeds are identified. Note that immune nodes are interpreted internally as Recovered (R) and are thus subject to waning (see below).

Compartment models

Compartment models are flexible models of diffusion or contagion, where nodes are compartmentalised into one of two or more categories.

The most basic model is the SI model. The SI model is the default in play_diffusion()/play_diffusions(), where nodes can only move from the Susceptible (S) category to the Infected (I) category. Whether nodes move from S to I depends on whether they are exposed to the infection, for instance through a contact, the transmissibility of the disease, and their thresholds to the disease.

Another common model is the SIR model. Here nodes move from S to I, as above, but additionally they can move from I to a Recovered (R) status. The probability that an infected node recovers at a timepoint is controlled by the recovery parameter.

The next most common models are the SIS and SIRS models. Here nodes move from S to I or additionally to R, as above, but additionally they can move from I or R back to a Susceptible (S) state. This probability is governed by the waning parameter. Where recover > 0 and waning = 1, the Recovery (R) state will be skipped and the node will return immediately to the Susceptible (S) compartment.

Lastly, these functions also offer the possibility of specifying a latency period in which nodes have been infected but are not yet infectious. Where latency > 0, an additional Exposed (E) compartment is introduced that governs the probability that a node moves from this E compartment to infectiousness (I). This can be used in in SEI, SEIS, SEIR, and SEIRS models.

See Also

Other makes: make_cran, make_create, make_ego, make_explicit, make_learning, make_motifs, make_random, make_read, make_stochastic, make_write

Other models: make_learning

Other diffusion: measure_diffusion_infection, measure_diffusion_net, measure_diffusion_node, member_diffusion

Examples

smeg <- generate_smallworld(15, 0.025)
  plot(play_diffusion(smeg, recovery = 0.4))
  #graphr(play_diffusion(ison_karateka))
# plot(play_diffusions(smeg, times = 10))

Description

These functions are similar to the ⁠create_*⁠ functions, but include some element of randomisation. They are particularly useful for creating a distribution of networks for exploring or testing network properties.

• generate_random() generates a random network with ties appearing at some probability.

• generate_configuration() generates a random network consistent with a given degree distribution.

• generate_man() generates a random network conditional on the dyad census of Mutual, Asymmetric, and Null dyads, respectively.

• generate_utilities() generates a random utility matrix.

These functions can create either one-mode or two-mode networks. To create a one-mode network, pass the main argument n a single integer, indicating the number of nodes in the network. To create a two-mode network, pass n a vector of two integers, where the first integer indicates the number of nodes in the first mode, and the second integer indicates the number of nodes in the second mode. As an alternative, an existing network can be provided to n and the number of modes, nodes, and directedness will be inferred.

Usage

generate_random(n, p = 0.5, directed = FALSE, with_attr = TRUE)

generate_configuration(.data)

generate_man(n, man = NULL)

generate_utilities(n, steps = 1, volatility = 0, threshold = 0)

generate_permutation(.data, with_attr = TRUE)

Arguments

Value

By default a tbl_graph object is returned, but this can be coerced into other types of objects using as_edgelist(), as_matrix(), as_tidygraph(), or as_network().

By default, all networks are created as undirected. This can be overruled with the argument directed = TRUE. This will return a directed network in which the arcs are out-facing or equivalent. This direction can be swapped using to_redirected(). In two-mode networks, the directed argument is ignored.

References

On random networks

Erdos, Paul, and Alfred Renyi. 1959. "On Random Graphs I" Publicationes Mathematicae. 6: 290–297.

On configuration models

Bollobas, Bela. 1980. "A Probabilistic Proof of an Asymptotic Formula for the Number of Labelled Regular Graphs". European Journal of Combinatorics 1: 311-316.

On dyad-census conditioned networks

Holland, Paul W., and Samuel Leinhardt. 1976. “Local Structure in Social Networks.” In D. Heise (Ed.), Sociological Methodology, pp 1-45. San Francisco: Jossey-Bass.

See Also

Other makes: make_cran, make_create, make_ego, make_explicit, make_learning, make_motifs, make_play, make_read, make_stochastic, make_write

Examples

graphr(generate_random(12, 0.4))
# graphr(generate_random(c(6, 6), 0.4))

Description

Researchers regularly need to work with a variety of external data formats. The following functions offer ways to import from some common external file formats into objects that {manynet} and other graph/network packages in R can work with:

• read_matrix() imports adjacency matrices from Excel/csv files.

• read_edgelist() imports edgelists from Excel/csv files.

• read_nodelist() imports nodelists from Excel/csv files.

• read_pajek() imports Pajek (.net or .paj) files.

• read_ucinet() imports UCINET files from the header (.##h).

• read_dynetml() imports DyNetML interchange format for rich social network data.

• read_graphml() imports GraphML files.

Usage

read_matrix(file = file.choose(), sv = c("comma", "semi-colon"), ...)

read_edgelist(file = file.choose(), sv = c("comma", "semi-colon"), ...)

read_nodelist(file = file.choose(), sv = c("comma", "semi-colon"), ...)

read_pajek(file = file.choose(), ties = NULL, ...)

read_ucinet(file = file.choose())

read_dynetml(file = file.choose())

read_graphml(file = file.choose())

read_gml(file = file.choose())

Arguments

Details

Note that these functions are not as actively maintained as others in the package, so please let us know if any are not currently working for you or if there are missing import routines by raising an issue on Github.

There are a number of repositories for network data that hold various datasets in different formats. See for example:

• UCINET data

• Pajek data

• networkdata

• GML datasets

• UCIrvine Network Data Repository

• SNAP Stanford Large Network Dataset Collection

Please let us know if you identify any further repositories of social or political networks and we would be happy to add them here.

The ⁠_ucinet⁠ functions only work with relatively recent UCINET file formats, e.g. type 6406 files. To import earlier UCINET file types, you will need to update them first. To import multiple matrices packed into a single UCINET file, you will need to unpack them and convert them one by one.

Value

read_edgelist() and read_nodelist() will import into edgelist (tibble) format which can then be coerced or combined into different graph objects from there.

read_pajek() and read_ucinet() will import into a tidygraph format, since they already contain both edge and attribute data. read_matrix() will import into tidygraph format too. Note that all graphs can be easily coerced into other formats with {manynet}'s as_ methods.

Source

read_ucinet() kindly supplied by Christian Steglich, constructed on 18 June 2015.

See Also

as

Other makes: make_cran, make_create, make_ego, make_explicit, make_learning, make_motifs, make_play, make_random, make_stochastic, make_write

Description

These functions are similar to the ⁠create_*⁠ functions, but include some element of randomisation. They are particularly useful for creating a distribution of networks for exploring or testing network properties.

• generate_smallworld() generates a small-world structure via ring rewiring at some probability.

• generate_scalefree() generates a scale-free structure via preferential attachment at some probability.

• generate_fire() generates a forest fire model.

• generate_islands() generates an islands model.

• generate_citations() generates a citations model.

These functions can create either one-mode or two-mode networks. To create a one-mode network, pass the main argument n a single integer, indicating the number of nodes in the network. To create a two-mode network, pass n a vector of two integers, where the first integer indicates the number of nodes in the first mode, and the second integer indicates the number of nodes in the second mode. As an alternative, an existing network can be provided to n and the number of modes, nodes, and directedness will be inferred.

Usage

generate_smallworld(n, p = 0.05, directed = FALSE, width = 2)

generate_scalefree(n, p = 1, directed = FALSE)

generate_fire(n, contacts = 1, their_out = 0, their_in = 1, directed = FALSE)

generate_islands(n, islands = 2, p = 0.5, bridges = 1, directed = FALSE)

generate_citations(
  n,
  ties = sample(1:4, 1),
  agebins = max(1, n/10),
  directed = FALSE
)

Arguments

Value

By default a tbl_graph object is returned, but this can be coerced into other types of objects using as_edgelist(), as_matrix(), as_tidygraph(), or as_network().

By default, all networks are created as undirected. This can be overruled with the argument directed = TRUE. This will return a directed network in which the arcs are out-facing or equivalent. This direction can be swapped using to_redirected(). In two-mode networks, the directed argument is ignored.

References

On small-world networks

Watts, Duncan J., and Steven H. Strogatz. 1998. “Collective Dynamics of ‘Small-World’ Networks.” Nature 393(6684):440–42. doi:10.1038/30918.

On scale-free networks

Barabasi, Albert-Laszlo, and Reka Albert. 1999. “Emergence of Scaling in Random Networks.” Science 286(5439):509–12. doi:10.1126/science.286.5439.509.

On the forest-fire model

Leskovec, Jure, Jon Kleinberg, and Christos Faloutsos. 2007. "Graph evolution: Densification and shrinking diameters". ACM transactions on Knowledge Discovery from Data, 1(1): 2-es.

See Also

Other makes: make_cran, make_create, make_ego, make_explicit, make_learning, make_motifs, make_play, make_random, make_read, make_write

Examples

graphr(generate_smallworld(12, 0.025))
graphr(generate_smallworld(12, 0.25))
graphr(generate_scalefree(12, 0.25))
graphr(generate_scalefree(12, 1.25))
generate_fire(10)
generate_islands(10)
generate_citations(10)

Description

Researchers may want to save or work with networks outside R. The following functions offer ways to export to some common external file formats:

• write_matrix() exports an adjacency matrix to a .csv file.

• write_edgelist() exports an edgelist to a .csv file.

• write_nodelist() exports a nodelist to a .csv file.

• write_pajek() exports Pajek .net files.

• write_ucinet() exports a pair of UCINET files in V6404 file format (.##h, .##d).

• write_graphml() exports GraphML files.

Usage

write_matrix(.data, filename, ...)

write_edgelist(.data, filename, ...)

write_nodelist(.data, filename, ...)

write_pajek(.data, filename, ...)

write_ucinet(.data, filename, name)

write_graphml(.data, filename, ...)

Arguments

Details

Note that these functions are not as actively maintained as others in the package, so please let us know if any are not currently working for you or if there are missing import routines by raising an issue on Github.

Value

The write_functions export to different file formats, depending on the function.

A pair of UCINET files in V6404 file format (.##h, .##d)

Source

write_ucinet() kindly supplied by Christian Steglich, constructed on 18 June 2015.

See Also

as

Other makes: make_cran, make_create, make_ego, make_explicit, make_learning, make_motifs, make_play, make_random, make_read, make_stochastic

Description

The as_ functions in {manynet} coerce objects of any of the following common classes of social network objects in R into the declared class:

• as_edgelist() coerces the object into an edgelist, as data frames or tibbles.

• as_matrix() coerces the object into an adjacency (one-mode/unipartite) or incidence (two-mode/bipartite) matrix.

• as_igraph() coerces the object into an {igraph} graph object.

• as_tidygraph() coerces the object into a {tidygraph} tbl_graph object.

• as_network() coerces the object into a {network} network object.

• as_siena() coerces the (igraph/tidygraph) object into a SIENA dependent variable.

• as_graphAM() coerces the object into a graph adjacency matrix.

• as_diffusion() coerces a table of diffusion events into a diff_model object similar to the output of play_diffusion().

• as_diffnet() coerces a diff_model object into a {netdiffuseR} diffnet object.

An effort is made for all of these coercion routines to be as lossless as possible, though some object classes are better at retaining certain kinds of information than others. Note also that there are some reserved column names in one or more object classes, which could otherwise lead to some unexpected results.

Usage

as_nodelist(.data)

as_edgelist(.data, twomode = FALSE)

as_matrix(.data, twomode = NULL)

as_igraph(.data, twomode = FALSE)

as_tidygraph(.data, twomode = FALSE)

as_network(.data, twomode = FALSE)

as_siena(.data, twomode = FALSE)

as_graphAM(.data, twomode = NULL)

as_diffusion(.data, twomode = FALSE, events)

as_diffnet(.data, twomode = FALSE)

Arguments

Details

Edgelists are expected to be held in data.frame or tibble class objects. The first two columns of such an object are expected to be the senders and receivers of a tie, respectively, and are typically named "from" and "to" (even in the case of an undirected network). These columns can contain integers to identify nodes or character strings/factors if the network is labelled. If the sets of senders and receivers overlap, a one-mode network is inferred. If the sets contain no overlap, a two-mode network is inferred. If a third, numeric column is present, a weighted network will be created.

Matrices can be either adjacency (one-mode) or incidence (two-mode) matrices. Incidence matrices are typically inferred from unequal dimensions, but since in rare cases a matrix with equal dimensions may still be an incidence matrix, an additional argument twomode can be specified to override this heuristic.

This information is usually already embedded in {igraph}, {tidygraph}, and {network} objects.

Value

The currently implemented coercions or translations are:

as_diffusion() and play_diffusion() return a 'diff_model' object that contains two different tibbles (tables) – a table of diffusion events and a table of the number of nodes in each relevant component (S, E, I, or R) – as well as a copy of the network upon which the diffusion ran. By default, a compact version of the component table is printed (to print all the changes at each time point, use print(..., verbose = T)). To retrieve the diffusion events table, use summary(...).

See Also

Other modifications: manip_correlation, manip_deformat, manip_from, manip_levels, manip_miss, manip_nodes, manip_paths, manip_permutation, manip_preformat, manip_project, manip_reformat, manip_scope, manip_split, manip_ties

Examples

test <- data.frame(from = c("A","B","B","C","C"), to = c("I","G","I","G","H"))
as_edgelist(test)
as_matrix(test)
as_igraph(test)
as_tidygraph(test)
as_network(test)
  # How to create a diff_model object from (basic) observed data
  events <- data.frame(t = c(0,1,1,2,3), 
                       nodes = c(1,2,3,2,4), 
                       event = c("I","I","I","R","I"))
  as_diffusion(create_filled(4), events = events)

Description

This function performs a Pearson pairwise correlation on a given matrix or network data. It includes a switch: whereas for a two-mode network it will perform a regular correlation, including all rows, for an undirected network it will perform a correlation on a matrix with the diagonals removed, for a reciprocated network it will include the difference between reciprocated ties, and for complex networks it will include also the difference between the self ties in each pairwise calculation. This function runs in $O(mn^2)$ complexity.

Usage

to_correlation(.data, method = NULL)

to_cosine(.data)

Arguments

See Also

Other modifications: manip_as, manip_deformat, manip_from, manip_levels, manip_miss, manip_nodes, manip_paths, manip_permutation, manip_preformat, manip_project, manip_reformat, manip_scope, manip_split, manip_ties

Description

These functions reformat manynet-consistent data.

• to_unnamed() reformats labelled network data to unlabelled network data.

• to_undirected() reformats directed network data to an undirected network, so that any pair of nodes with at least one directed edge will be connected by an undirected edge in the new network. This is equivalent to the "collapse" mode in {igraph}..

• to_unweighted() reformats weighted network data to unweighted network data, with all tie weights removed.

• to_unsigned() reformats signed network data to unsigned network data keeping just the "positive" or "negative" ties.

• to_simplex() reformats complex network data, containing loops, to simplex network data, without any loops.

• to_uniplex() reformats multiplex network data to a single type of tie.

If the format condition is not met, for example to_undirected() is used on a network that is already undirected, the network data is returned unaltered. No warning is given so that these functions can be used to ensure conformance.

Unlike the ⁠as_*()⁠ group of functions, these functions always return the same class as they are given, only transforming these objects' properties.

Usage

to_unnamed(.data)

to_undirected(.data)

to_unweighted(.data, threshold = 1)

to_unsigned(.data, keep = c("positive", "negative"))

to_simplex(.data)

to_uniplex(.data, tie)

Arguments

Details

Not all functions have methods available for all object classes. Below are the currently implemented S3 methods:

Value

All to_ functions return an object of the same class as that provided. So passing it an igraph object will return an igraph object and passing it a network object will return a network object, with certain modifications as outlined for each function.

See Also

Other modifications: manip_as, manip_correlation, manip_from, manip_levels, manip_miss, manip_nodes, manip_paths, manip_permutation, manip_preformat, manip_project, manip_reformat, manip_scope, manip_split, manip_ties

Examples

as_tidygraph(create_filled(5)) %>%
  mutate_ties(type = sample(c("friend", "enemy"), 10, replace = TRUE)) %>%
  to_uniplex("friend")

Description

These functions offer tools for joining lists of manynet-consistent objects (matrices, igraph, tidygraph, or network objects) into a single object.

• from_subgraphs() modifies a list of subgraphs into a single tidygraph.

• from_egos() modifies a list of ego networks into a whole tidygraph

• from_waves() modifies a list of network waves into a longitudinal tidygraph.

• from_slices() modifies a list of time slices of a network into a dynamic tidygraph.

• from_ties() modifies a list of different ties into a multiplex tidygraph

Usage

from_subgraphs(netlist)

from_egos(netlist)

from_waves(netlist)

from_slices(netlist, remove.duplicates = FALSE)

from_ties(netlist, netnames)

Arguments

Value

A tidygraph object combining the list of network data.

See Also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_levels, manip_miss, manip_nodes, manip_paths, manip_permutation, manip_preformat, manip_project, manip_reformat, manip_scope, manip_split, manip_ties

Examples

ison_adolescents %>%
  mutate(unicorn = sample(c("yes", "no"), 8, replace = TRUE)) %>%
  to_subgraphs(attribute = "unicorn") %>%
  from_subgraphs()
ison_adolescents %>%
  to_egos() %>%
  from_egos()
ison_adolescents %>%
  mutate_ties(wave = sample(1:4, 10, replace = TRUE)) %>%
  to_waves(attribute = "wave") %>%
  from_waves()
ison_adolescents %>%
  mutate_ties(time = 1:10, increment = 1) %>% 
  add_ties(c(1,2), list(time = 3, increment = -1)) %>% 
  to_slices(slice = c(5,7)) %>%
  from_slices()

Description

These functions reformat the levels in manynet-consistent network data.

• to_onemode() reformats two-mode network data into one-mode network data by simply removing the nodeset 'type' information. Note that this is not the same as to_mode1() or to_mode2().

• to_twomode() reformats one-mode network data into two-mode network data, using a mark to distinguish the two sets of nodes.

• to_multilevel() reformats two-mode network data into multimodal network data, which allows for more levels and ties within modes.

If the format condition is not met, for example to_onemode() is used on a network that is already one-mode, the network data is returned unaltered. No warning is given so that these functions can be used to ensure conformance.

Unlike the ⁠as_*()⁠ group of functions, these functions always return the same class as they are given, only transforming these objects' properties.

Usage

to_onemode(.data)

to_twomode(.data, mark)

to_multilevel(.data)

Arguments

Details

Not all functions have methods available for all object classes. Below are the currently implemented S3 methods:

Value

All to_ functions return an object of the same class as that provided. So passing it an igraph object will return an igraph object and passing it a network object will return a network object, with certain modifications as outlined for each function.

See Also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_from, manip_miss, manip_nodes, manip_paths, manip_permutation, manip_preformat, manip_project, manip_reformat, manip_scope, manip_split, manip_ties

Description

These functions offer tools for imputing missing tie data. Currently two options are available:

• na_to_zero() replaces any missing values with zeros, which are the modal value in sparse social networks.

• na_to_mean() replaces missing values with the average non-missing value.

Usage

na_to_zero(.data)

na_to_mean(.data)

Arguments

Value

A data object of the same class as the function was given.

References

On missing data

Krause, Robert, Mark Huisman, Christian Steglich, and Tom A.B. Snijders. 2020. "Missing data in cross-sectional networks: An extensive comparison of missing data treatment methods". Social Networks, 62: 99-112. doi:10.1016/j.socnet.2020.02.004

See Also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_from, manip_levels, manip_nodes, manip_paths, manip_permutation, manip_preformat, manip_project, manip_reformat, manip_scope, manip_split, manip_ties

Examples

missTest <- ison_adolescents %>% 
   add_tie_attribute("weight", c(1,NA,NA,1,1,1,NA,NA,1,1)) %>% 
   as_matrix
missTest
na_to_zero(missTest)
na_to_mean(missTest)

Description

These functions allow users to add and edit information about the network itself. This includes the name, year, and mode of collection of the network, as well as definitions of the nodes and ties in the network. Where available, this information is printed for tidygraph-class objects, and can be used for printing a grand table in the {grand} package.

Usage

add_info(.data, ...)

Arguments

Examples

add_info(ison_algebra, name = "Algebra")

Description

These functions allow users to add and delete nodes and their attributes:

• add_nodes() adds an additional number of nodes to network data.

• delete_nodes() deletes nodes from network data.

• add_node_attribute(), mutate(), or mutate_nodes() offer ways to add a vector of values to a network as a nodal attribute.

• rename_nodes() and rename() rename nodal attributes.

• bind_node_attributes() appends all nodal attributes from one network to another, and join_nodes() merges all nodal attributes from one network to another.

• filter_nodes() subsets nodes based on some nodal attribute-related logical statement.

Note that while ⁠add_*()⁠/⁠delete_*()⁠ functions operate similarly as comparable {igraph} functions, ⁠mutate*()⁠, ⁠bind*()⁠, etc work like {tidyverse} or {dplyr}-style functions.

Usage

add_nodes(.data, nodes, attribute = NULL)

delete_nodes(.data, nodes)

add_node_attribute(.data, attr_name, vector)

mutate_nodes(.data, ...)

mutate(.data, ...)

bind_node_attributes(.data, object2)

join_nodes(
  .data,
  object2,
  .by = NULL,
  join_type = c("full", "left", "right", "inner")
)

rename_nodes(.data, ...)

rename(.data, ...)

filter_nodes(.data, ..., .by)

Arguments

Details

Not all functions have methods available for all object classes. Below are the currently implemented S3 methods:

Value

A data object of the same class as the function was given.

See Also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_from, manip_levels, manip_miss, manip_paths, manip_permutation, manip_preformat, manip_project, manip_reformat, manip_scope, manip_split, manip_ties

Examples

other <- create_filled(4) %>% mutate(name = c("A", "B", "C", "D"))
  add_nodes(other, 4, list(name = c("Matthew", "Mark", "Luke", "Tim")))
  other <- create_filled(4) %>% mutate(name = c("A", "B", "C", "D"))
  another <- create_filled(3) %>% mutate(name = c("E", "F", "G"))
  join_nodes(another, other)

Description

These functions return tidygraphs containing only special sets of ties:

• to_matching() returns only the matching ties in some network data.

• to_mentoring() returns only ties to nodes' closest mentors.

• to_eulerian() returns only the Eulerian path within some network data.

• to_tree() returns the spanning tree in some network data or, if the data is unconnected, a forest of spanning trees.

• to_dominating() returns the dominating tree of the network

Usage

to_matching(.data, mark = "type")

to_mentoring(.data, elites = 0.1)

to_eulerian(.data)

to_tree(.data)

to_dominating(.data, from, direction = c("out", "in"))

Arguments

Details

Not all functions have methods available for all object classes. Below are the currently implemented S3 methods:

Value

All to_ functions return an object of the same class as that provided. So passing it an igraph object will return an igraph object and passing it a network object will return a network object, with certain modifications as outlined for each function.

to_matching()

to_matching() uses {igraph}'s max_bipartite_match() to return a network in which each node is only tied to one of its previous ties. The number of these ties left is its cardinality, and the algorithm seeks to maximise this such that, where possible, each node will be associated with just one node in the other mode or some other mark. The algorithm used is the push-relabel algorithm with greedy initialization and a global relabelling after every $\frac{n}{2}$ steps, where $n$ is the number of nodes in the network.

References

On matching

Goldberg, Andrew V., and Robert E. Tarjan. 1986. "A new approach to the maximum flow problem". Proceedings of the eighteenth annual ACM symposium on Theory of computing – STOC '86. 136-146. doi:10.1145/12130.12144

On mentoring

Valente, Thomas, and Rebecca Davis. 1999. "Accelerating the Diffusion of Innovations Using Opinion Leaders", Annals of the American Academy of Political and Social Science 566: 56-67. doi:10.1177/000271629956600105

On Eulerian trails

Euler, Leonard. 1736. "Solutio problematis ad geometriam situs pertinentis". Comment. Academiae Sci. I. Petropolitanae 8: 128–140.

Hierholzer, Carl. 1873. "Ueber die Möglichkeit, einen Linienzug ohne Wiederholung und ohne Unterbrechung zu umfahren". Mathematische Annalen, 6(1): 30–32. doi:10.1007/BF01442866

On minimum spanning trees

Boruvka, Otakar. 1926. "O jistem problemu minimalnim". Prace Mor. Prirodoved. Spol. V Brne III 3: 37-58.

Kruskal, Joseph B. 1956. "On the shortest spanning subtree of a graph and the travelling salesman problem". Proceedings of the American Mathematical Society 7(1): 48-50. doi:10.1090/S0002-9939-1956-0078686-7

Prim, R.C. 1957. "Shortest connection networks and some generalizations". Bell System Technical Journal 36(6):1389-1401. doi:10.1002/j.1538-7305.1957.tb01515.x

See Also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_from, manip_levels, manip_miss, manip_nodes, manip_permutation, manip_preformat, manip_project, manip_reformat, manip_scope, manip_split, manip_ties

Examples

to_matching(ison_southern_women)
#graphr(to_matching(ison_southern_women))
graphr(to_mentoring(ison_adolescents))
  to_eulerian(delete_nodes(ison_koenigsberg, "Lomse"))
  #graphr(to_eulerian(delete_nodes(ison_koenigsberg, "Lomse")))

Description

to_permuted() permutes the network using a Fisher-Yates shuffle on both the rows and columns (for a one-mode network) or on each of the rows and columns (for a two-mode network).

Usage

to_permuted(.data, with_attr = TRUE)

Arguments

See Also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_from, manip_levels, manip_miss, manip_nodes, manip_paths, manip_preformat, manip_project, manip_reformat, manip_scope, manip_split, manip_ties

Examples

graphr(ison_adolescents, node_size = 4)
graphr(to_permuted(ison_adolescents), node_size = 4)

Description

These functions add some format to manynet-consistent data.

• to_directed() reformats undirected network data to a directed network.

• to_redirected() reformats the direction of directed network data, flipping any existing direction.

• to_reciprocated() reformats directed network data such that every directed tie is reciprocated.

• to_acyclic() reformats network data to an acyclic graph.

• to_named() reformats unlabelled network data to labelled network data from a vector of names or random baby names.

• to_signed() reformats unsigned network data to signed network data with signs from a mark vector or at random.

If the format condition is not met, for example to_undirected() is used on a network that is already undirected, the network data is returned unaltered. No warning is given so that these functions can be used to ensure conformance.

Unlike the ⁠as_*()⁠ group of functions, these functions always return the same class as they are given, only transforming these objects' properties.

Usage

to_named(.data, names = NULL)

to_signed(.data, mark = NULL)

to_weighted(.data, measure = NULL)

Arguments

Details

Not all functions have methods available for all object classes. Below are the currently implemented S3 methods:

Value

All to_ functions return an object of the same class as that provided. So passing it an igraph object will return an igraph object and passing it a network object will return a network object, with certain modifications as outlined for each function.

See Also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_from, manip_levels, manip_miss, manip_nodes, manip_paths, manip_permutation, manip_project, manip_reformat, manip_scope, manip_split, manip_ties

Description

These functions offer tools for projecting manynet-consistent data:

• to_mode1() projects a two-mode network to a one-mode network of the first node set's (e.g. rows) joint affiliations to nodes in the second node set (columns).

• to_mode2() projects a two-mode network to a one-mode network of the second node set's (e.g. columns) joint affiliations to nodes in the first node set (rows).

• to_ties() projects a network to one where the ties become nodes and incident nodes become their ties.

Usage

to_mode1(.data, similarity = c("count", "jaccard", "rand", "pearson", "yule"))

to_mode2(.data, similarity = c("count", "jaccard", "rand", "pearson", "yule"))

to_ties(.data)

Arguments

Details

Not all functions have methods available for all object classes. Below are the currently implemented S3 methods:

Value

All to_ functions return an object of the same class as that provided. So passing it an igraph object will return an igraph object and passing it a network object will return a network object, with certain modifications as outlined for each function.

See Also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_from, manip_levels, manip_miss, manip_nodes, manip_paths, manip_permutation, manip_preformat, manip_reformat, manip_scope, manip_split, manip_ties

Examples

to_mode1(ison_southern_women)
to_mode2(ison_southern_women)
#graphr(to_mode1(ison_southern_women))
#graphr(to_mode2(ison_southern_women))
to_ties(ison_adolescents)
#graphr(to_ties(ison_adolescents))

Description

These functions reformat manynet-consistent data.

• to_acyclic() reformats network data to an acyclic graph.

• to_anti() reformats network data into its complement, where only ties not present in the original network are included in the new network.

• to_redirected() reformats the direction of directed network data, flipping any existing direction.

• to_reciprocated() reformats directed network data such that every directed tie is reciprocated.

Unlike the ⁠as_*()⁠ group of functions, these functions always return the same class as they are given, only transforming these objects' properties.

Usage

to_acyclic(.data)

to_anti(.data)

to_redirected(.data)

to_reciprocated(.data)

to_directed(.data)

Arguments

Details

Not all functions have methods available for all object classes. Below are the currently implemented S3 methods:

Value

All to_ functions return an object of the same class as that provided. So passing it an igraph object will return an igraph object and passing it a network object will return a network object, with certain modifications as outlined for each function.

Functions

• to_redirected(): Returns an object that has any edge direction transposed, or flipped, so that senders become receivers and receivers become senders. This essentially has no effect on undirected networks or reciprocated ties.

• to_reciprocated(): Returns an object where all ties are reciprocated.

See Also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_from, manip_levels, manip_miss, manip_nodes, manip_paths, manip_permutation, manip_preformat, manip_project, manip_scope, manip_split, manip_ties

Examples

to_anti(ison_southern_women)
#graphr(to_anti(ison_southern_women))

Description

These functions offer tools for transforming manynet-consistent objects (matrices, igraph, tidygraph, or network objects). Transforming means that the returned object may have different dimensions than the original object.

• to_ego() scopes a network into the local neighbourhood of a given node.

• to_giant() scopes a network into one including only the main component and no smaller components or isolates.

• to_no_isolates() scopes a network into one excluding all nodes without ties

• to_subgraph() scopes a network into a subgraph by filtering on some node-related logical statement.

• to_blocks() reduces a network to ties between a given partition membership vector.

Usage

to_ego(.data, node, max_dist = 1, min_dist = 0, direction = c("out", "in"))

to_giant(.data)

to_no_isolates(.data)

to_subgraph(.data, ...)

to_blocks(.data, membership, FUN = mean)

Arguments

Details

Not all functions have methods available for all object classes. Below are the currently implemented S3 methods:

Value

All to_ functions return an object of the same class as that provided. So passing it an igraph object will return an igraph object and passing it a network object will return a network object, with certain modifications as outlined for each function.

to_blocks()

Reduced graphs provide summary representations of network structures by collapsing groups of connected nodes into single nodes while preserving the topology of the original structures.

See Also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_from, manip_levels, manip_miss, manip_nodes, manip_paths, manip_permutation, manip_preformat, manip_project, manip_reformat, manip_split, manip_ties

Examples

ison_adolescents %>%
  mutate_ties(wave = sample(1995:1998, 10, replace = TRUE)) %>%
  to_waves(attribute = "wave") %>%
  to_no_isolates()

Description

These functions offer tools for splitting manynet-consistent objects (matrices, igraph, tidygraph, or network objects) into lists of networks.

Not all functions have methods available for all object classes. Below are the currently implemented S3 methods:

Usage

to_egos(.data, max_dist = 1, min_dist = 0, direction = c("out", "in"))

to_subgraphs(.data, attribute)

to_components(.data)

to_waves(.data, attribute = "wave", panels = NULL, cumulative = FALSE)

to_slices(.data, attribute = "time", slice = NULL)

Arguments

Value

The returned object will be a list of network objects.

Functions

• to_egos(): Returns a list of ego (or focal) networks.

• to_subgraphs(): Returns a list of subgraphs on some given node attribute.

• to_components(): Returns a list of the components in a network.

• to_waves(): Returns a network with some discrete observations over time into a list of those observations.

• to_slices(): Returns a list of a network with some continuous time variable at some time slice(s).

See Also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_from, manip_levels, manip_miss, manip_nodes, manip_paths, manip_permutation, manip_preformat, manip_project, manip_reformat, manip_scope, manip_ties

Examples

to_egos(ison_adolescents)
  # graphs(to_egos(ison_adolescents,2))
ison_adolescents %>%
  mutate(unicorn = sample(c("yes", "no"), 8,
                          replace = TRUE)) %>%
  to_subgraphs(attribute = "unicorn")
  to_components(ison_marvel_relationships)
ison_adolescents %>%
  mutate_ties(wave = sample(1995:1998, 10, replace = TRUE)) %>%
  to_waves(attribute = "wave")
ison_adolescents %>%
  mutate_ties(time = 1:10, increment = 1) %>% 
  add_ties(c(1,2), list(time = 3, increment = -1)) %>% 
  to_slices(slice = 7)

Description

These functions allow users to add and delete ties and their attributes:

• add_ties() adds additional ties to network data

• delete_ties() deletes ties from network data

• add_tie_attribute() and mutate_ties() offer ways to add a vector of values to a network as a tie attribute.

• rename_ties() renames tie attributes.

• bind_ties() appends the tie data from two networks and join_ties() merges ties from two networks, adding a tie attribute identifying the newly added ties.

• filter_ties() subsets ties based on some tie attribute-related logical statement.

Note that while ⁠add_*()⁠/⁠delete_*()⁠ functions operate similarly as comparable {igraph} functions, ⁠mutate*()⁠, ⁠bind*()⁠, etc work like {tidyverse} or {dplyr}-style functions.

Usage

add_ties(.data, ties, attribute = NULL)

delete_ties(.data, ties)

add_tie_attribute(.data, attr_name, vector)

mutate_ties(.data, ...)

rename_ties(.data, ...)

arrange_ties(.data, ...)

bind_ties(.data, ...)

join_ties(.data, object2, attr_name)

filter_ties(.data, ...)

select_ties(.data, ...)

summarise_ties(.data, ...)

Arguments

Value

A tidygraph (tbl_graph) data object.

See Also

Other modifications: manip_as, manip_correlation, manip_deformat, manip_from, manip_levels, manip_miss, manip_nodes, manip_paths, manip_permutation, manip_preformat, manip_project, manip_reformat, manip_scope, manip_split

Examples

other <- create_filled(4) %>% mutate(name = c("A", "B", "C", "D"))
  mutate_ties(other, form = 1:6) %>% filter_ties(form < 4)
  add_tie_attribute(other, "weight", c(1, 2, 2, 2, 1, 2))
ison_adolescents %>% add_ties(c("Betty","Tina")) %>% graphr()
delete_ties(ison_adolescents, 3)
delete_ties(ison_adolescents, "Alice|Sue")

Description

This function provides users with an easy way to graph (m)any network data for exploration, investigation, inspiration, and communication.

It builds upon {ggplot2} and {ggraph} to offer pretty and extensible graphing solutions. However, compared to those solutions, graphr() contains various algorithms to provide better looking graphs by default. This means that just passing the function some network data will often be sufficient to return a reasonable-looking graph.

The function also makes it easy to modify many of the most commonly adapted aspects of a graph, including node and edge size, colour, and shape, as arguments rather than additional functions that you need to remember. These can be defined outright, e.g. node_size = 8, or in reference to an attribute of the network, e.g. node_size = "wealth".

Lastly, graphr() uses {ggplot2}-related theme information, so it is easy to make colour palette and fonts institution-specific and consistent. See e.g. theme_iheid() for more.

To learn more about what can be done visually, try run_tute("Visualisation").

Usage

graphr(
  .data,
  layout,
  labels = TRUE,
  node_color,
  node_shape,
  node_size,
  node_group,
  edge_color,
  edge_size,
  snap = FALSE,
  ...,
  node_colour,
  edge_colour
)

Arguments

Value

A ggplot2::ggplot() object. The last plot can be saved to the file system using ggplot2::ggsave().

See Also

Other mapping: map_graphs, map_grapht, map_layout_configuration, map_layout_partition

Examples

graphr(ison_adolescents)
ison_adolescents %>%
  mutate(color = rep(c("introvert","extrovert"), times = 4),
         size = ifelse(node_is_cutpoint(ison_adolescents), 6, 3)) %>%
  mutate_ties(ecolor = rep(c("friends", "acquaintances"), times = 5)) %>%
  graphr(node_color = "color", node_size = "size",
         edge_size = 1.5, edge_color = "ecolor")

Description

This function provides users with an easy way to graph lists of network data for comparison.

It builds upon this package's graphr() function, and inherits all the same features and arguments. See graphr() for more. However, it uses the {patchwork} package to plot the graphs side by side and, if necessary, in successive rows. This is useful for lists of networks that represent, for example, ego or component subgraphs of a network, or a list of a network's different types of tie or across time. By default just the first and last network will be plotted, but this can be overridden by the "waves" parameter.

Where the graphs are of the same network (same nodes), the graphs may share a layout to facilitate comparison. By default, successive graphs will use the layout calculated for the "first" network, but other options include the "last" layout, or a mix, "both", of them.

Usage

graphs(netlist, waves, based_on = c("first", "last", "both"), ...)

Arguments

Value

Multiple ggplot2::ggplot() objects displayed side-by-side.

See Also

Other mapping: map_graphr, map_grapht, map_layout_configuration, map_layout_partition

Examples

#graphs(to_egos(ison_adolescents))
#graphs(to_egos(ison_adolescents), waves = 8)
#graphs(to_egos(ison_adolescents), waves = c(2, 4, 6))
#graphs(play_diffusion(ison_adolescents))

Description

This function provides users with an easy way to graph dynamic network data for exploration and presentation.

It builds upon this package's graphr() function, and inherits all the same features and arguments. See graphr() for more. However, it uses the {gganimate} package to animate the changes between successive iterations of a network. This is useful for networks in which the ties and/or the node or tie attributes are changing.

A progress bar is shown if it takes some time to encoding all the .png files into a .gif.

Usage

grapht(
  tlist,
  keep_isolates = TRUE,
  layout,
  labels = TRUE,
  node_color,
  node_shape,
  node_size,
  edge_color,
  edge_size,
  ...,
  node_colour,
  edge_colour
)

Arguments

Value

Shows a .gif image. Assigning the result of the function saves the gif to a temporary folder and the object holds the path to this file.

Source

https://blog.schochastics.net/posts/2021-09-15_animating-network-evolutions-with-gganimate/

See Also

Other mapping: map_graphr, map_graphs, map_layout_configuration, map_layout_partition

Examples

#ison_adolescents %>%
#  mutate_ties(year = sample(1995:1998, 10, replace = TRUE)) %>%
#  to_waves(attribute = "year", cumulative = TRUE) %>%
#  grapht()
#ison_adolescents %>% 
#  mutate(gender = rep(c("male", "female"), times = 4),
#         hair = rep(c("black", "brown"), times = 4),
#         age = sample(11:16, 8, replace = TRUE)) %>%
#  mutate_ties(year = sample(1995:1998, 10, replace = TRUE),
#              links = sample(c("friends", "not_friends"), 10, replace = TRUE),
#              weekly_meetings = sample(c(3, 5, 7), 10, replace = TRUE)) %>%
#  to_waves(attribute = "year") %>%
#  grapht(layout = "concentric", membership = "gender",
#             node_shape = "gender", node_color = "hair",
#             node_size =  "age", edge_color = "links",
#             edge_size = "weekly_meetings")
#grapht(play_diffusion(ison_adolescents, seeds = 5))

Description

Configurational layouts locate nodes at symmetric coordinates to help illustrate particular configurations. Currently "triad" and "quad" layouts are available. The "configuration" layout will choose the appropriate configurational layout automatically.

Usage

layout_tbl_graph_configuration(.data, circular = FALSE, times = 1000)

layout_tbl_graph_dyad(.data, circular = FALSE, times = 1000)

layout_tbl_graph_triad(.data, circular = FALSE, times = 1000)

layout_tbl_graph_tetrad(.data, circular = FALSE, times = 1000)

layout_tbl_graph_pentad(.data, circular = FALSE, times = 1000)

layout_tbl_graph_hexad(.data, circular = FALSE, times = 1000)

Arguments

See Also

Other mapping: map_graphr, map_graphs, map_grapht, map_layout_partition

Description

These algorithms layout networks based on two or more partitions, and are recommended for use with graphr() or {ggraph}. Note that these layout algorithms use {Rgraphviz}, a package that is only available on Bioconductor. It will first need to be downloaded using BiocManager::install("Rgraphviz"). If it has not already been installed, there is a prompt the first time these functions are used though.

The "hierarchy" layout layers the first node set along the bottom, and the second node set along the top, sequenced and spaced as necessary to minimise edge overlap. The "alluvial" layout is similar to "hierarchy", but places successive layers horizontally rather than vertically. The "railway" layout is similar to "hierarchy", but nodes are aligned across the layers. The "ladder" layout is similar to "railway", but places successive layers horizontally rather than vertically. The "concentric" layout places a "hierarchy" layout around a circle, with successive layers appearing as concentric circles. The "multilevel" layout places successive layers as multiple levels. The "lineage" layout ranks nodes in Y axis according to values.

Usage

layout_tbl_graph_hierarchy(
  .data,
  center = NULL,
  circular = FALSE,
  times = 1000
)

layout_tbl_graph_alluvial(.data, circular = FALSE, times = 1000)

layout_tbl_graph_railway(.data, circular = FALSE, times = 1000)

layout_tbl_graph_ladder(.data, circular = FALSE, times = 1000)

layout_tbl_graph_concentric(
  .data,
  membership,
  radius = NULL,
  order.by = NULL,
  circular = FALSE,
  times = 1000
)

layout_tbl_graph_multilevel(.data, level, circular = FALSE)

layout_tbl_graph_lineage(.data, rank, circular = FALSE)

Arguments

Source

Diego Diez, Andrew P. Hutchins and Diego Miranda-Saavedra. 2014. "Systematic identification of transcriptional regulatory modules from protein-protein interaction networks". Nucleic Acids Research, 42 (1) e6.

See Also

Other mapping: map_graphr, map_graphs, map_grapht, map_layout_configuration

Examples

#graphr(ison_southern_women, layout = "hierarchy", center = "events",
#           node_color = "type", node_size = 3)
#graphr(ison_southern_women, layout = "alluvial")
#graphr(ison_southern_women, layout = "concentric", membership = "type",
#           node_color = "type", node_size = 3)
#graphr(ison_lotr, layout = "multilevel",
#           node_color = "Race", level = "Race", node_size = 3)
# ison_adolescents %>%
#   mutate(year = rep(c(1985, 1990, 1995, 2000), times = 2),
#          cut = node_is_cutpoint(ison_adolescents)) %>%
#   graphr(layout = "lineage", rank = "year", node_color = "cut",
#              node_size = migraph::node_degree(ison_adolescents)*10)

Description

Many palettes generator

Usage

many_palettes(palette, n, type = c("discrete", "continuous"))

Arguments

Value

A graphic display of colours in palette.

Source

Adapted from https://github.com/karthik/wesanderson/blob/master/R/colors.R

Examples

many_palettes()
#many_palettes("IHEID")

Description

These functions enable to add color scales to be graphs.

Usage

scale_fill_iheid(direction = 1, ...)

scale_colour_iheid(direction = 1, ...)

scale_color_iheid(direction = 1, ...)

scale_edge_colour_iheid(direction = 1, ...)

scale_edge_color_iheid(direction = 1, ...)

scale_fill_centres(direction = 1, ...)

scale_colour_centres(direction = 1, ...)

scale_color_centres(direction = 1, ...)

scale_edge_colour_centres(direction = 1, ...)

scale_edge_color_centres(direction = 1, ...)

scale_fill_sdgs(direction = 1, ...)

scale_colour_sdgs(direction = 1, ...)

scale_color_sdgs(direction = 1, ...)

scale_edge_colour_sdgs(direction = 1, ...)

scale_edge_color_sdgs(direction = 1, ...)

scale_fill_ethz(direction = 1, ...)

scale_colour_ethz(direction = 1, ...)

scale_color_ethz(direction = 1, ...)

scale_edge_colour_ethz(direction = 1, ...)

scale_edge_color_ethz(direction = 1, ...)

scale_fill_uzh(direction = 1, ...)

scale_colour_uzh(direction = 1, ...)

scale_color_uzh(direction = 1, ...)

scale_edge_colour_uzh(direction = 1, ...)

scale_edge_color_uzh(direction = 1, ...)

scale_fill_rug(direction = 1, ...)

scale_colour_rug(direction = 1, ...)

scale_color_rug(direction = 1, ...)

scale_edge_colour_rug(direction = 1, ...)

scale_edge_color_rug(direction = 1, ...)

Arguments

Examples

#ison_brandes %>%
#mutate(core = migraph::node_is_core(ison_brandes)) %>%
#graphr(node_color = "core") +
#scale_color_iheid()
#graphr(ison_physicians[[1]], edge_color = "type") +
#scale_edge_color_ethz()

Description

These functions enable graphs to be easily and quickly themed, e.g. changing the default colour of the graph's vertices and edges.

Usage

set_manynet_theme(theme = "default")

theme_iheid(base_size = 12, base_family = "serif")

theme_ethz(base_size = 12, base_family = "sans")

theme_uzh(base_size = 12, base_family = "sans")

theme_rug(base_size = 12, base_family = "mono")

Arguments

Examples

to_mentoring(ison_brandes) %>%
  mutate(color = c(rep(c(1,2,3), 3), 3)) %>%
  graphr(node_color = "color") +
  labs(title = "Who leads and who follows?") +
  scale_color_iheid() +
  theme_iheid()

Description

These functions identify nodes belonging to (some level of) the core of a network:

• node_is_core() assigns nodes to either the core or periphery.

• node_coreness() assigns nodes to their level of k-coreness.

Usage

node_is_core(.data, method = c("degree", "eigenvector"))

node_coreness(.data)

Arguments

Core-periphery

This function is used to identify which nodes should belong to the core, and which to the periphery. It seeks to minimize the following quantity:

$Z(S_1) = \sum_{(i<j)\in S_1} \textbf{I}_{\{A_{ij}=0\}} + \sum_{(i<j)\notin S_1} \textbf{I}_{\{A_{ij}=1\}}$

where nodes $\{i,j,...,n\}$ are ordered in descending degree, $A$ is the adjacency matrix, and the indicator function is 1 if the predicate is true or 0 otherwise. Note that minimising this quantity maximises density in the core block and minimises density in the periphery block; it ignores ties between these blocks.

References

On core-periphery partitioning

Borgatti, Stephen P., & Everett, Martin G. 1999. Models of core /periphery structures. Social Networks, 21, 375–395. doi:10.1016/S0378-8733(99)00019-2

Lip, Sean Z. W. 2011. “A Fast Algorithm for the Discrete Core/Periphery Bipartitioning Problem.” doi:10.48550/arXiv.1102.5511

See Also

Other memberships: member_brokerage, member_cliques, member_community_hier, member_community_non, member_components, member_equivalence

Examples

node_is_core(ison_adolescents)
#ison_adolescents %>% 
#   mutate(corep = node_is_core()) %>% 
#   graphr(node_color = "corep")
node_coreness(ison_adolescents)

Description

These functions return logical vectors the length of the nodes in a network identifying which hold certain properties or positions in the network.

• node_is_infected() marks nodes that are infected by a particular time point.

• node_is_exposed() marks nodes that are exposed to a given (other) mark.

• node_is_latent() marks nodes that are latent at a particular time point.

• node_is_recovered() marks nodes that are recovered at a particular time point.

Usage

node_is_latent(diff_model, time = 0)

node_is_infected(diff_model, time = 0)

node_is_recovered(diff_model, time = 0)

node_is_exposed(.data, mark)

Arguments

Exposed

node_is_exposed() is similar to node_exposure(), but returns a mark (TRUE/FALSE) vector indicating which nodes are currently exposed to the diffusion content. This diffusion content can be expressed in the 'mark' argument. If no 'mark' argument is provided, and '.data' is a diff_model object, then the function will return nodes exposure to the seed nodes in that diffusion.

See Also

Other marks: mark_nodes, mark_select, mark_tie_select, mark_ties, mark_triangles

Examples

# To mark nodes that are latent by a particular time point
  node_is_latent(play_diffusion(create_tree(6), latency = 1), time = 1)
  # To mark nodes that are infected by a particular time point
  node_is_infected(play_diffusion(create_tree(6)), time = 1)
  # To mark nodes that are recovered by a particular time point
  node_is_recovered(play_diffusion(create_tree(6), recovery = 0.5), time = 3)
  # To mark which nodes are currently exposed
  (expos <- node_is_exposed(manynet::create_tree(14), mark = c(1,3)))
  which(expos)

Description

These functions implement logical tests for various network features.

• is_connected() tests whether network is strongly connected, or weakly connected if undirected.

• is_perfect_matching() tests whether there is a matching for a network that covers every node in the network.

• is_eulerian() tests whether there is a Eulerian path for a network where that path passes through every tie exactly once.

• is_acyclic() tests whether network is a directed acyclic graph.

• is_aperiodic() tests whether network is aperiodic.

Usage

is_connected(.data)

is_perfect_matching(.data, mark = "type")

is_eulerian(.data)

is_acyclic(.data)

is_aperiodic(.data, max_path_length = 4)

Arguments

Value

TRUE if the condition is met, or FALSE otherwise.

is_connected

To test weak connection on a directed network, please see to_undirected().

is_perfect_matching

For two-mode or bipartite networks, to_matching() is used to identify whether a perfect matching is possible. For one-mode networks, we use the Tutte theorem. Note that currently only subgraphs with cutpoints removed are tested, and not all possible subgraphs. This is to avoid computationally expensive combinatorial operations, but may come at the cost of some edge cases where a one-mode network cannot perfectly match as suggested.

Source

https://stackoverflow.com/questions/55091438/r-igraph-find-all-cycles

References

On perfect matching

Tutte, William T. 1950. "The factorization of locally finite graphs". Canadian Journal of Mathematics. 2: 44–49. doi:10.4153/cjm-1950-005-2

On aperiodicity

Jarvis, J.P, and D.R. Shier. 1996. "Graph-theoretic analysis of finite Markov chains", in Shier, D.R., Wallenius, K.T. (eds) Applied Mathematical Modeling: A Multidisciplinary Approach. CRC Press.

See Also

Other marking: mark_format, mark_is

Examples

is_connected(ison_southern_women)
is_perfect_matching(ison_southern_women)
is_eulerian(ison_brandes)
is_acyclic(ison_algebra)
is_aperiodic(ison_algebra)

Description

These functions implement logical tests for various network properties. All ⁠is_*()⁠ functions return a logical scalar (TRUE or FALSE).

• is_twomode() marks networks TRUE if they contain two sets of nodes.

• is_weighted() marks networks TRUE if they contain tie weights.

• is_directed() marks networks TRUE if the ties specify which node is the sender and which the receiver.

• is_labelled() marks networks TRUE if there is a 'names' attribute for the nodes.

• is_attributed() marks networks TRUE if there are other nodal attributes than 'names' or 'type'.

• is_signed() marks networks TRUE if the ties can be either positive or negative.

• is_complex() marks networks TRUE if any ties are loops, with the sender and receiver being the same node.

• is_multiplex() marks networks TRUE if it contains multiple types of ties, such that there can be multiple ties between the same sender and receiver.

• is_uniplex() marks networks TRUE if it is neither complex nor multiplex.

Usage

is_twomode(.data)

is_weighted(.data)

is_directed(.data)

is_labelled(.data)

is_signed(.data)

is_complex(.data)

is_multiplex(.data)

is_uniplex(.data)

is_attributed(.data)

Arguments

See Also

Other marking: mark_features, mark_is

Examples

is_twomode(create_filled(c(2,2)))
is_weighted(create_tree(3))
is_directed(create_tree(2))
is_directed(create_tree(2, directed = TRUE))
is_labelled(create_empty(3))
is_signed(create_lattice(3))
is_complex(create_lattice(4))
is_multiplex(create_filled(c(3,3)))
is_uniplex(create_star(3))
is_attributed(ison_algebra)

Description

These functions implement logical tests for networks' classes.

• is_manynet() marks a network TRUE if it is compatible with {manynet} functions.

• is_edgelist() marks a network TRUE if it is an edgelist.

• is_graph() marks a network TRUE if it contains graph-level information.

• is_list() marks a network TRUE if it is a (non-igraph) list of networks, for example a set of ego networks or a dynamic or longitudinal set of networks.

• is_longitudinal() marks a network TRUE if it contains longitudinal, panel data.

• is_dynamic() marks a network TRUE if it contains dynamic, time-stamped data

All ⁠is_*()⁠ functions return a logical scalar (TRUE or FALSE).

Usage

is_manynet(.data)

is_graph(.data)

is_edgelist(.data)

is_list(.data)

is_longitudinal(.data)

is_dynamic(.data)

Arguments

Value

TRUE if the condition is met, or FALSE otherwise.

See Also

Other marking: mark_features, mark_format

Examples

is_manynet(create_filled(2))
is_graph(create_star(2))
is_edgelist(matrix(c(2,2), 1, 2))
is_edgelist(as_edgelist(matrix(c(2,2), 1, 2)))
is_longitudinal(create_tree(5, 3))
is_dynamic(create_tree(3))

Description

These functions return logical vectors the length of the nodes in a network identifying which hold certain properties or positions in the network.

• node_is_isolate() marks nodes that are isolates, with neither incoming nor outgoing ties.

• node_is_independent() marks nodes that are members of the largest independent set, aka largest internally stable set.

• node_is_cutpoint() marks nodes that cut or act as articulation points in a network, increasing the number of connected components when removed.

• node_is_core() marks nodes that are members of the network's core.

• node_is_fold() marks nodes that are in a structural fold between two or more triangles that are only connected by that node.

• node_is_mentor() marks a proportion of high indegree nodes as 'mentors' (see details).

Usage

node_is_isolate(.data)

node_is_pendant(.data)

node_is_independent(.data)

node_is_cutpoint(.data)

node_is_fold(.data)

node_is_mentor(.data, elites = 0.1)

node_is_neighbor(.data, node)

Arguments

References

On independent sets

Tsukiyama, Shuji, Mikio Ide, Hiromu Ariyoshi, and Isao Shirawaka. 1977. "A new algorithm for generating all the maximal independent sets". SIAM Journal on Computing, 6(3):505–517. doi:10.1137/0206036

On articulation or cut-points

Tarjan, Robert E. and Uzi Vishkin. 1985. "An Efficient Parallel Biconnectivity Algorithm", SIAM Journal on Computing 14(4): 862-874. doi:10.1137/0214061

On structural folds

Vedres, Balazs, and David Stark. 2010. "Structural folds: Generative disruption in overlapping groups", American Journal of Sociology 115(4): 1150-1190. doi:10.1086/649497

On mentoring

Valente, Thomas, and Rebecca Davis. 1999. "Accelerating the Diffusion of Innovations Using Opinion Leaders", Annals of the American Academy of Political and Social Science 566: 56-67.

See Also

Other marks: mark_diff, mark_select, mark_tie_select, mark_ties, mark_triangles

Examples

node_is_isolate(ison_brandes)
node_is_independent(ison_adolescents)
node_is_cutpoint(ison_brandes)
node_is_fold(create_explicit(A-B, B-C, A-C, C-D, C-E, D-E))

Description

These functions return logical vectors the length of the nodes in a network identifying which hold certain properties or positions in the network.

• node_is_random() marks one or more nodes at random.

• node_is_max() and node_is_min() are more generally useful for converting the results from some node measure into a mark-class object. They can be particularly useful for highlighting which node or nodes are key because they minimise or, more often, maximise some measure.

Usage

node_is_random(.data, size = 1)

node_is_max(node_measure, ranks = 1)

node_is_min(node_measure, ranks = 1)

Arguments

See Also

Other marks: mark_diff, mark_nodes, mark_tie_select, mark_ties, mark_triangles

Examples

node_is_random(ison_brandes, 2)
#node_is_max(migraph::node_degree(ison_brandes))
#node_is_min(migraph::node_degree(ison_brandes))