Utils

graph_tiger.utils.get_adjacency_spectrum(graph, k=inf, eigvals_only=False, which='LA', use_gpu=False)

Gets the top k eigenpairs of the adjacency matrix

Parameters

• graph – undirected NetworkX graph

• k – number of top k eigenpairs to obtain

• eigvals_only – get only the eigenvalues i.e., no eigenvectors

• which – the type of k eigenvectors and eigenvalues to find

Returns

the eigenpair information

graph_tiger.utils.get_laplacian(graph)

Gets the Laplacian matrix in sparse CSR format

Parameters

graph – undirected NetworkX graph

Returns

Scipy sparse Laplacian matrix

graph_tiger.utils.get_laplacian_spectrum(graph, k=inf, which='SM', tol=0.01, eigvals_only=True, use_gpu=False)

Gets the bottom k eigenpairs of the Laplacian matrix

Parameters

• graph – undirected NetworkX graph

• k – number of bottom k eigenpairs to obtain

• which – he type of k eigenvectors and eigenvalues to find

• tol – the precision at which to stop computing the eigenpairs

• eigvals_only – get only the eigenvalues i.e., no eigenvectors

Returns

the eigenpair information

graph_tiger.utils.get_sparse_graph(graph)

Returns a sparse adjacency matrix in CSR format

Parameters

graph – undirected NetworkX graph

Returns

Scipy sparse adjacency matrix

graph_tiger.utils.gpu_available()