Package: spectralGraphTopology 0.2.3

Ze Vinicius

spectralGraphTopology: Learning Graphs from Data via Spectral Constraints

In the era of big data and hyperconnectivity, learning high-dimensional structures such as graphs from data has become a prominent task in machine learning and has found applications in many fields such as finance, health care, and networks. 'spectralGraphTopology' is an open source, documented, and well-tested R package for learning graphs from data. It provides implementations of state of the art algorithms such as Combinatorial Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation based on Majorization-Minimization (GLE-MM), and Graph Estimation based on Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph learning has been widely employed for clustering, where specific algorithms are available in the literature. To this end, we provide an implementation of the Constrained Laplacian Rank (CLR) algorithm.

Authors:Ze Vinicius [cre, aut], Daniel P. Palomar [aut]

spectralGraphTopology_0.2.3.tar.gz
spectralGraphTopology_0.2.3.tar.gz(r-4.5-noble)spectralGraphTopology_0.2.3.tar.gz(r-4.4-noble)
spectralGraphTopology.pdf |spectralGraphTopology.html
spectralGraphTopology/json (API)
NEWS

# Install 'spectralGraphTopology' in R:
install.packages('spectralGraphTopology', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/dppalomar/spectralgraphtopology/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

openblascpp

4.61 score 2 stars 1 packages 135 scripts 757 downloads 24 exports 22 dependencies

Last updated 3 years agofrom:5a4a5889ab. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 04 2024
R-4.5-linux-x86_64OKDec 04 2024

Exports:AaccuracyAstarblock_diagcluster_k_component_graphDDstarfdrfscoreLlearn_bipartite_graphlearn_bipartite_k_component_graphlearn_combinatorial_graph_laplacianlearn_graph_sigreplearn_k_component_graphlearn_laplacian_gle_admmlearn_laplacian_gle_mmlearn_smooth_approx_graphlearn_smooth_graphLstarnpvrecallrelative_errorspecificity

Dependencies:clicrayondata.tablegluehmsjsonlitelatticelifecycleMASSMatrixpkgconfigprettyunitsprogressR6RcppRcppArmadilloRcppEigenrlangrlistvctrsXMLyaml

Learning graphs from data via spectral constraints (html)

Rendered fromSpectralGraphTopology.html.asisusingR.rsp::asison Dec 04 2024.

Last update: 2019-05-08
Started: 2019-05-08

Readme and manuals

Help Manual

Help pageTopics
Package spectralGraphTopologyspectralGraphTopology-package
Computes the Adjacency linear operator which maps a vector of weights into a valid Adjacency matrix.A
Computes the accuracy between two matricesaccuracy
Computes the Astar operator.Astar
Constructs a block diagonal matrix from a list of square matricesblock_diag
Cluster a k-component graph from data using the Constrained Laplacian Rank algorithm Cluster a k-component graph on the basis of an observed data matrix. Check out https://mirca.github.io/spectralGraphTopology for code examples.cluster_k_component_graph
Computes the degree operator from the vector of edge weights.D
Computes the Dstar operator, i.e., the adjoint of the D operator.Dstar
Computes the false discovery rate between two matricesfdr
Computes the fscore between two matricesfscore
Computes the Laplacian linear operator which maps a vector of weights into a valid Laplacian matrix.L
Learn a bipartite graph Learns a bipartite graph on the basis of an observed data matrixlearn_bipartite_graph
Learns a bipartite k-component graph Jointly learns the Laplacian and Adjacency matrices of a graph on the basis of an observed data matrixlearn_bipartite_k_component_graph
Learn the Combinatorial Graph Laplacian from data Learns a graph Laplacian matrix using the Combinatorial Graph Laplacian (CGL) algorithm proposed by Egilmez et. al. (2017)learn_combinatorial_graph_laplacian
Learn graphs from a smooth signal representation approach This function learns a graph from a observed data matrix using the method proposed by Dong (2016).learn_graph_sigrep
Learn the Laplacian matrix of a k-component graph Learns a k-component graph on the basis of an observed data matrix. Check out https://mirca.github.io/spectralGraphTopology for code examples.learn_k_component_graph
Learn the weighted Laplacian matrix of a graph using the ADMM methodlearn_laplacian_gle_admm
Learn the weighted Laplacian matrix of a graph using the MM methodlearn_laplacian_gle_mm
Learns a smooth approximated graph from an observed data matrix. Check out https://mirca.github.io/spectralGraphTopology for code examples.learn_smooth_approx_graph
Learn a graph from smooth signals This function learns a connected graph given an observed signal matrix using the method proposed by Kalofilias (2016).learn_smooth_graph
Computes the Lstar operator.Lstar
Computes the negative predictive value between two matricesnpv
Computes the recall between two matricesrecall
Computes the relative error between the true and estimated matricesrelative_error
Computes the specificity between two matricesspecificity