adjacency_matrix

adjacency_matrix(G, nodelist=None, dtype=None, weight='weight')

Returns adjacency matrix of G.

Parameters:

Ggraph

A NetworkX graph

nodelistlist, 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().

dtypeNumPy data-type, optional

The desired data-type for the array. If None, then the NumPy default is used.

weightstring or None, optional (default=’weight’)

The edge data key used to provide each value in the matrix. If None, then each edge has weight 1.

Returns:

ASciPy sparse matrix

Adjacency matrix representation of G.

See also

to_numpy_array

to_scipy_sparse_array

to_dict_of_dicts

adjacency_spectrum

Notes

For directed graphs, entry i,j corresponds to an edge from i to j.

If you want a pure Python adjacency matrix representation try networkx.convert.to_dict_of_dicts which will return a dictionary-of-dictionaries format that can be addressed as a sparse matrix.

For MultiGraph/MultiDiGraph with parallel edges the weights are summed. See to_numpy_array for other options.

The convention used for self-loop edges in graphs is to assign the diagonal matrix entry value to the edge weight attribute (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.adjacency_matrix(G)
>>> print(A.todense())
[[1]]
>>> A.setdiag(A.diagonal() * 2)
>>> print(A.todense())
[[2]]