Package: PAFit 1.2.10

Thong Pham

PAFit: Generative Mechanism Estimation in Temporal Complex Networks

Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015) <doi:10.1371/journal.pone.0137796>. Thong Pham et al. (2016) <doi:10.1038/srep32558>. Thong Pham et al. (2020) <doi:10.18637/jss.v092.i03>. Thong Pham et al. (2021) <doi:10.1093/comnet/cnab024>.

Authors:Thong Pham, Paul Sheridan, Hidetoshi Shimodaira

PAFit_1.2.10.tar.gz
PAFit_1.2.10.tar.gz(r-4.5-noble)PAFit_1.2.10.tar.gz(r-4.4-noble)
PAFit_1.2.10.tgz(r-4.4-emscripten)PAFit_1.2.10.tgz(r-4.3-emscripten)
PAFit.pdf |PAFit.html
PAFit/json (API)
NEWS

# Install 'PAFit' in R:
install.packages('PAFit', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/thongphamthe/pafit/issues1 issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • coauthor.author_id - A collaboration network between authors of papers in the field of complex networks with article time-stamps
  • coauthor.net - A collaboration network between authors of papers in the field of complex networks with article time-stamps
  • coauthor.truetime - A collaboration network between authors of papers in the field of complex networks with article time-stamps

On CRAN:

Conda:

cppopenmp

2.70 score 457 downloads 5 mentions 22 exports 55 dependencies

Last updated 1 years agofrom:5d111ba8b1. Checks:2 OK, 1 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 24 2025
R-4.5-linux-x86_64NOTEMar 24 2025
R-4.4-linux-x86_64OKMar 24 2025

Exports:as.PAFit_netfrom_igraphfrom_networkDynamicgenerate_BAgenerate_BBgenerate_ERgenerate_fit_onlygenerate_netgenerate_simulated_data_from_estimated_modelget_statisticsgraph_from_filegraph_to_fileJeongjoint_estimateNewmanonly_A_estimateonly_F_estimatePAFit_oneshotplot_contributiontest_linear_PAto_igraphto_networkDynamic

Dependencies:celestialclicodacolorspacecpp11dplyrevaluatefansifarvergenericsggplot2gluegtablehighrigraphisobandknitrlabelinglatticelifecyclemagicaxismagrittrmapprojmapsMASSMatrixmgcvmunsellnetworknetworkDynamicnetworkLiteNISTunitsnlmepillarpkgconfigplotrixplyrpracmaR6RANNRColorBrewerRcpprlangscalessmstatnet.commontibbletidyselectutf8vctrsVGAMviridisLitewithrxfunyaml

PAFit: an R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks

Rendered fromTutorial.pdf.asisusingR.rsp::asison Mar 24 2025.

Last update: 2018-09-19
Started: 2015-03-27

Citation

To cite PAFit in publications use:

Pham T, Sheridan P, Shimodaira H (2020). “PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks.” Journal of Statistical Software, 92(3), 1–30. doi:10.18637/jss.v092.i03.

If you use the package to estimate node fitnesses, please consider to cite also:

Pham T, Sheridan P, Shimodaira H (2016). “Joint Estimation of Preferential Attachment and Node Fitness in Growing Complex Networks.” Scientific Reports, 6, 32558 EP. doi:10.1038/srep32558.

If you use the package to estimate preferential attachment from a temporal network, please consider to cite also:

Pham T, Sheridan P, Shimodaira H (2015). “PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks.” PLOS ONE, 10, 1-18. doi:10.1371/journal.pone.0137796.

If you use the package to estimate preferential attachment from one network snapshot, please consider to cite also:

Pham T, Sheridan P, Shimodaira H (2021). “Non-parametric estimation of the preferential attachment function from one network snapshot.” Journal of Complex Networks, 9. doi:10.1093/comnet/cnab024.

Corresponding BibTeX entries:

  @Article{,
    title = {{PAFit}: An {R} Package for the Non-Parametric Estimation
      of Preferential Attachment and Node Fitness in Temporal Complex
      Networks},
    author = {Thong Pham and Paul Sheridan and Hidetoshi Shimodaira},
    journal = {Journal of Statistical Software},
    year = {2020},
    volume = {92},
    number = {3},
    pages = {1--30},
    doi = {10.18637/jss.v092.i03},
  }
  @Article{,
    author = {Thong Pham and Paul Sheridan and Hidetoshi Shimodaira},
    journal = {Scientific Reports},
    title = {Joint Estimation of Preferential Attachment and Node
      Fitness in Growing Complex Networks},
    year = {2016},
    month = {9},
    volume = {6},
    pages = {32558 EP},
    publisher = {Nature Publishing Group},
    doi = {10.1038/srep32558},
  }
  @Article{,
    author = {Thong Pham and Paul Sheridan and Hidetoshi Shimodaira},
    journal = {PLOS ONE},
    title = {PAFit: A Statistical Method for Measuring Preferential
      Attachment in Temporal Complex Networks},
    year = {2015},
    month = {9},
    volume = {10},
    pages = {1-18},
    publisher = {Public Library of Science},
    doi = {10.1371/journal.pone.0137796},
  }
  @Article{,
    author = {Thong Pham and Paul Sheridan and Hidetoshi Shimodaira},
    journal = {Journal of Complex Networks},
    title = {Non-parametric estimation of the preferential attachment
      function from one network snapshot},
    year = {2021},
    month = {10},
    volume = {9},
    publisher = {Oxford University Press},
    doi = {10.1093/comnet/cnab024},
  }

Readme and manuals

Help Manual

Help pageTopics
Generative Mechanism Estimation in Temporal Complex NetworksPAFit-package PAFit
Converting an edgelist matrix to a PAFit_net objectas.PAFit_net
A collaboration network between authors of papers in the field of complex networks with article time-stampscoauthor.author_id coauthor.net coauthor.truetime ComplexNetCoauthor
Convert an igraph object to a PAFit_net objectfrom_igraph
Convert a networkDynamic object to a PAFit_net objectfrom_networkDynamic
Simulating networks from the generalized Barabasi-Albert modelgenerate_BA
Simulating networks from the Bianconi-Barabasi modelgenerate_BB
Simulating networks from the Erdos-Renyi modelgenerate_ER
Simulating networks from the Caldarelli modelgenerate_fit_only
Simulating networks from preferential attachment and fitness mechanismsgenerate_net
Generating simulated data from a fitted modelgenerate_simulated_data_from_estimated_model
Getting summarized statistics from input dataget_statistics PAFit_data
Read file to a PAFit_net objectgraph_from_file
Write the graph in a PAFit_net object to filegraph_to_file
Jeong's method for estimating the preferential attachment functionJeong
Joint inference of attachment function and node fitnessesjoint_estimate
Corrected Newman's method for estimating the preferential attachment functionNewman
Estimating the attachment function in isolation by PAFit methodonly_A_estimate
Estimating node fitnesses in isolationonly_F_estimate
Estimating the nonparametric preferential attachment function from one single snapshot.PAFit_oneshot
Plotting contributions calculated from the observed data and contributions calculated from simulated dataplot_contribution
Plotting the estimated attachment function and node fitnessplot.Full_PAFit_result
Plotting the estimated attachment functionplot.PA_result
Plot a 'PAFit_net' objectplot.PAFit_net
Plotting the estimated attachment function and node fitness of a 'PAFit_result' objectplot.PAFit_result
Printing simple information of the cross-validation dataprint.CV_Data
Printing simple information of the cross-validation resultprint.CV_Result
printing information on the estimation resultprint.Full_PAFit_result
Printing information of the estimated attachment functionprint.PA_result
Printing simple information on the statistics of the network stored in a 'PAFit_data' objectprint.PAFit_data
Printing simple information of a 'PAFit_net' objectprint.PAFit_net
printing information on the estimation result stored in a 'PAFit_result' objectprint.PAFit_result
Printing summary information of the cross-validation datasummary.CV_Data
Output summary information of the cross-validation resultsummary.CV_Result
Summary information on the estimation resultsummary.Full_PAFit_result
Summary of the estimated attachment functionsummary.PA_result
Output summary information on the statistics of the network stored in a 'PAFit_data' objectsummary.PAFit_data
Summary information of a 'PAFit_net' objectsummary.PAFit_net
Output summary information on the estimation result stored in a 'PAFit_result' objectsummary.PAFit_result
Fitting various distributions to a degree vectortest_linear_PA
Convert a PAFit_net object to an igraph objectto_igraph
Convert a PAFit_net object to a networkDynamic objectto_networkDynamic