#

Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

#

networkx.convert_matrix.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 matrix are interpreted as the weight of a single edge joining the vertices.

  • create_using (NetworkX graph constructor, optional (default=nx.Graph)) – Graph type to create. If graph instance, then cleared before populated.

  • 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

For directed graphs, explicitly mention create_using=nx.DiGraph, and entry i,j of A corresponds to an edge from i to j.

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

If create_using indicates 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 as sp
>>> A = sp.sparse.eye(2, 2, 1)
>>> G = nx.from_scipy_sparse_matrix(A)

If create_using indicates a multigraph and the matrix has only integer entries and parallel_edges is False, then the entries will be treated as weights for edges joining the nodes (without creating parallel edges):

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

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

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