# Learning Graphs from Data via Spectral Constraints

## Documentation for package ‘spectralGraphTopology’ version 0.2.3

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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