Learning Graphs from Data via Spectral Constraints

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Documentation for package ‘spectralGraphTopology’ version 0.2.3

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spectralGraphTopology-package Package spectralGraphTopology
A Computes the Adjacency linear operator which maps a vector of weights into a valid Adjacency matrix.
accuracy Computes the accuracy between two matrices
Astar Computes the Astar operator.
block_diag Constructs a block diagonal matrix from a list of square matrices
cluster_k_component_graph 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.
D Computes the degree operator from the vector of edge weights.
Dstar Computes the Dstar operator, i.e., the adjoint of the D operator.
fdr Computes the false discovery rate between two matrices
fscore Computes the fscore between two matrices
L Computes the Laplacian linear operator which maps a vector of weights into a valid Laplacian matrix.
learn_bipartite_graph Learn a bipartite graph Learns a bipartite graph on the basis of an observed data matrix
learn_bipartite_k_component_graph Learns a bipartite k-component graph Jointly learns the Laplacian and Adjacency matrices of a graph on the basis of an observed data matrix
learn_combinatorial_graph_laplacian 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_graph_sigrep 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_k_component_graph 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_laplacian_gle_admm Learn the weighted Laplacian matrix of a graph using the ADMM method
learn_laplacian_gle_mm Learn the weighted Laplacian matrix of a graph using the MM method
learn_smooth_approx_graph Learns a smooth approximated graph from an observed data matrix. Check out https://mirca.github.io/spectralGraphTopology for code examples.
learn_smooth_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).
Lstar Computes the Lstar operator.
npv Computes the negative predictive value between two matrices
recall Computes the recall between two matrices
relative_error Computes the relative error between the true and estimated matrices
specificity Computes the specificity between two matrices