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()