DGLHeteroGraph.adjacency_matrix(transpose=None, ctx=device(type='cpu'), scipy_fmt=None, etype=None)[source]

Return the adjacency matrix of edges of the given edge type.

By default, a row of returned adjacency matrix represents the destination of an edge and the column represents the source.

When transpose is True, a row represents the source and a column represents a destination.

  • transpose (bool, optional) – A flag to transpose the returned adjacency matrix. (Default: False)
  • ctx (context, optional) – The context of returned adjacency matrix. (Default: cpu)
  • scipy_fmt (str, optional) – If specified, return a scipy sparse matrix in the given format. Otherwise, return a backend dependent sparse tensor. (Default: None)
  • etype (str, optional) – The edge type. Can be omitted if there is only one edge type in the graph. (Default: None)

Adjacency matrix.

Return type:

SparseTensor or scipy.sparse.spmatrix


Instantiate a heterogeneous graph.

>>> follows_g = dgl.graph([(0, 0), (1, 1)], 'user', 'follows')
>>> devs_g = dgl.bipartite([(0, 0), (1, 2)], 'developer', 'develops', 'game')
>>> g = dgl.hetero_from_relations([follows_g, devs_g])

Get a backend dependent sparse tensor. Here we use PyTorch for example.

>>> g.adjacency_matrix(etype='develops')
tensor(indices=tensor([[0, 2],
                       [0, 1]]),
       values=tensor([1., 1.]),
       size=(3, 2), nnz=2, layout=torch.sparse_coo)

Get a scipy coo sparse matrix.

>>> g.adjacency_matrix(scipy_fmt='coo', etype='develops')
<3x2 sparse matrix of type '<class 'numpy.int64'>'
with 2 stored elements in COOrdinate format>