from_scipy_sparse_matrix¶

from_scipy_sparse_matrix(A, parallel_edges=False, create_using=None, edge_attribute='weight')[source]¶

Creates a new graph from an adjacency matrix given as a SciPy sparse matrix.

Parameters:

A (scipy sparse matrix) – An adjacency matrix representation of a graph
parallel_edges (Boolean) – If this is True, \(create_using\) is a multigraph, and \(A\) is an integer matrix, then entry (i, j) in the matrix is interpreted as the number of parallel edges joining vertices i and j in the graph. If it is False, then the entries in the adjacency matrix are interpreted as the weight of a single edge joining the vertices.
create_using (NetworkX graph) – Use specified graph for result. The default is Graph()
edge_attribute (string) – Name of edge attribute to store matrix numeric value. The data will have the same type as the matrix entry (int, float, (real,imag)).

Notes

If \(create_using\) is an instance of networkx.MultiGraph or networkx.MultiDiGraph, \(parallel_edges\) is True, and the entries of \(A\) are of type int, then this function returns a multigraph (of the same type as \(create_using\)) with parallel edges. In this case, \(edge_attribute\) will be ignored.

If \(create_using\) is an undirected multigraph, then only the edges indicated by the upper triangle of the matrix \(A\) will be added to the graph.

Examples

>>> import scipy.sparse
>>> A = scipy.sparse.eye(2,2,1)
>>> G = nx.from_scipy_sparse_matrix(A)

If \(create_using\) is a multigraph and the matrix has only integer entries, the entries will be interpreted as weighted edges joining the vertices (without creating parallel edges):

>>> import scipy
>>> A = scipy.sparse.csr_matrix([[1, 1], [1, 2]])
>>> G = nx.from_scipy_sparse_matrix(A, create_using=nx.MultiGraph())
>>> G[1][1]
{0: {'weight': 2}}

If \(create_using\) is a multigraph and the matrix has only integer entries but \(parallel_edges\) is True, then the entries will be interpreted as the number of parallel edges joining those two vertices:

>>> import scipy
>>> A = scipy.sparse.csr_matrix([[1, 1], [1, 2]])
>>> G = nx.from_scipy_sparse_matrix(A, parallel_edges=True,
...                                 create_using=nx.MultiGraph())
>>> G[1][1]
{0: {'weight': 1}, 1: {'weight': 1}}