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, andA
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
If
create_using
isnetworkx.MultiGraph
ornetworkx.MultiDiGraph
,parallel_edges
is True, and the entries ofA
are of typeint
, then this function returns a multigraph (constructed fromcreate_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 matrixA
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 andparallel_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 andparallel_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}})