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adjacency_matrix¶
- adjacency_matrix(G, nodelist=None, weight='weight')[source]¶
Return 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 : SciPy sparse matrix
Adjacency matrix representation of G.
See also
to_numpy_matrix, to_scipy_sparse_matrix, to_dict_of_dicts
Notes
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_matrix 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:
>>> 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]]