to_scipy_sparse_array(G, nodelist=None, dtype=None, weight='weight', format='csr')
For directed graphs, matrix entry i,j corresponds to an edge from i to j.
The matrix entries are populated using the edge attribute held in parameter weight. When an edge does not have that attribute, the value of the entry is 1.
For multiple edges the matrix values are the sums of the edge weights.
When :None:None:`nodelist` does not contain every node in G, the adjacency matrix is built from the subgraph of G that is induced by the nodes in :None:None:`nodelist`.
The convention used for self-loop edges in graphs is to assign the diagonal matrix entry value to the weight attribute of the edge (or the number 1 if the edge has no weight attribute). If the alternate convention of doubling the edge weight is desired the resulting Scipy sparse matrix can be modified as follows:
>>> G = nx.Graph([(1, 1)]) >>> A = nx.to_scipy_sparse_array(G) >>> print(A.todense()) [[1]] >>> A.setdiag(A.diagonal() * 2) >>> print(A.toarray()) [[2]]
The NetworkX graph used to construct the sparse matrix.
The rows and columns are ordered according to the nodes in :None:None:`nodelist`. If :None:None:`nodelist` is None, then the ordering is produced by G.nodes().
A valid NumPy dtype used to initialize the array. If None, then the NumPy default is used.
The edge attribute that holds the numerical value used for the edge weight. If None then all edge weights are 1.
The type of the matrix to be returned (default 'csr'). For some algorithms different implementations of sparse matrices can perform better. See for details.
Graph adjacency matrix.
Returns the graph adjacency matrix as a SciPy sparse array.
>>> G = nx.MultiDiGraph()
... G.add_edge(0, 1, weight=2) 0
>>> G.add_edge(1, 0) 0
>>> G.add_edge(2, 2, weight=3) 0
>>> G.add_edge(2, 2) 1
>>> S = nx.to_scipy_sparse_array(G, nodelist=[0, 1, 2])See :
... print(S.toarray()) [[0 2 0] [1 0 0] [0 0 4]]
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.linalg.graphmatrix.adjacency_matrix
networkx.convert_matrix.to_scipy_sparse_array
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