networkx.convert.to_numpy_matrix

to_numpy_matrix(G, nodelist=None, dtype=None, order=None)

Return the graph adjacency matrix as a NumPy matrix.

Parameters:

G : graph

The NetworkX graph used to construct the NumPy matrix.

nodelist : list, optional

The rows and columns are ordered according to the nodes in nodelist. If nodelist is None, then the ordering is produced by G.nodes().

dtype : NumPy data-type, optional

A valid NumPy dtype used to initialize the array. If None, then the NumPy default is used.

order : {‘C’, ‘F’}, optional

Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory. If None, then the NumPy default is used.

Returns:

M : NumPy matrix

Graph adjacency matrix.

Notes

The matrix entries are populated using the ‘weight’ edge attribute. When an edge does not have the ‘weight’ attribute, the value of the entry is 1. For multiple edges, the values of the entries are the sums of the edge attributes for each edge.

When nodelist does not contain every node in G, the matrix is built from the subgraph of G that is induced by the nodes in nodelist.

Examples

>>> G = nx.MultiDiGraph()
>>> G.add_edge(0,1,weight=2)
>>> G.add_edge(1,0)
>>> G.add_edge(2,2,weight=3)
>>> G.add_edge(2,2)
>>> nx.to_numpy_matrix(G, nodelist=[0,1,2])
matrix([[ 0.,  2.,  0.],
        [ 1.,  0.,  0.],
        [ 0.,  0.,  4.]])