networkx.linalg.graphmatrix.adjacency_matrix¶

adjacency_matrix
(G, nodelist=None, weight='weight')[source]¶ Returns adjacency matrix of G.
 Parameters
G (graph) – A NetworkX graph
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().
weight (string 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
A – Adjacency matrix representation of G.
 Return type
SciPy sparse matrix
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 dictionaryofdictionaries format that can be addressed as a sparse matrix.
For MultiGraph/MultiDiGraph with parallel edges the weights are summed. See to_numpy_matrix for other options.
The convention used for selfloop 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:
>>> import scipy as sp >>> G = nx.Graph([(1,1)]) >>> A = nx.adjacency_matrix(G) >>> print(A.todense()) [[1]] >>> A.setdiag(A.diagonal()*2) >>> print(A.todense()) [[2]]
See also
to_numpy_matrix()
,to_scipy_sparse_matrix()
,to_dict_of_dicts()
,adjacency_spectrum()