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, 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_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
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_array(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_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 andparallel_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.