from_scipy_sparse_matrix(A, parallel_edges=False, create_using=None, edge_attribute='weight')
For directed graphs, explicitly mention create_using=nx.DiGraph, and entry i,j of A corresponds to an edge from i to j.
If :None:None:`create_using` is networkx.MultiGraph
or networkx.MultiDiGraph
, :None:None:`parallel_edges` is True, and the entries of A are of type int
, then this function returns a multigraph (constructed from :None:None:`create_using`) with parallel edges. In this case, :None:None:`edge_attribute` will be ignored.
If :None:None:`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.
An adjacency matrix representation of a graph
If this is True, :None:None:`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.
Graph type to create. If graph instance, then cleared before populated.
Name of edge attribute to store matrix numeric value. The data will have the same type as the matrix entry (int, float, (real,imag)).
Creates a new graph from an adjacency matrix given as a SciPy sparse matrix.
>>> import scipy as sp
... import scipy.sparse # call as sp.sparse
... A = sp.sparse.eye(2, 2, 1)
... G = nx.from_scipy_sparse_matrix(A)
If :None:None:`create_using` indicates a multigraph and the matrix has only integer entries and :None:None:`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 :None:None:`create_using` indicates a multigraph and the matrix has only integer entries and :None:None:`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]])See :
... G = nx.from_scipy_sparse_matrix(
... A, parallel_edges=True, create_using=nx.MultiGraph
... )
... G[1][1] AtlasView({0: {'weight': 1}, 1: {'weight': 1}})
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.convert_matrix.from_scipy_sparse_matrix
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