- adjacency_matrix(G, nodelist=None, dtype=None, weight='weight')¶
Returns adjacency matrix of G.
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.
- ASciPy sparse matrix
Adjacency matrix representation of G.
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_arrayfor 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()) [] >>> A.setdiag(A.diagonal() * 2) >>> print(A.todense()) []