from_scipy_sparse_array#

from_scipy_sparse_array(A, parallel_edges=False, create_using=None, edge_attribute='weight')[source]#

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

Parameters:
A: scipy.sparse array

An adjacency matrix representation of a graph

parallel_edgesBoolean

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_usingNetworkX 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_array(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_array([[1, 1], [1, 2]])
>>> G = nx.from_scipy_sparse_array(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_array([[1, 1], [1, 2]])
>>> G = nx.from_scipy_sparse_array(
...     A, parallel_edges=True, create_using=nx.MultiGraph
... )
>>> G[1][1]
AtlasView({0: {'weight': 1}, 1: {'weight': 1}})
----

Additional backends implement this function

cugraph : GPU-accelerated backend.

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