DGLGraph.adj_external(transpose=False, ctx=device(type='cpu'), scipy_fmt=None, etype=None)[source]

Return the adjacency matrix in an external format, such as Scipy or backend dependent sparse tensor.

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

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

  • 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 or (str, str, str), optional) –

    The type names of the edges. The allowed type name formats are:

    • (str, str, str) for source node type, edge type and destination node type.

    • or one str edge type name if the name can uniquely identify a triplet format in the graph.

    Can be omitted if the graph has only one type of edges.


Adjacency matrix.

Return type:

SparseTensor or scipy.sparse.spmatrix


The following example uses PyTorch backend.

>>> import dgl
>>> import torch

Instantiate a heterogeneous graph.

>>> g = dgl.heterograph({
...     ('user', 'follows', 'user'): ([0, 1], [0, 1]),
...     ('developer', 'develops', 'game'): ([0, 1], [0, 2])
... })

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

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

Get a scipy coo sparse matrix.

>>> g.adj_external(scipy_fmt='coo', etype='develops')
<2x3 sparse matrix of type '<class 'numpy.int64'>'
   with 2 stored elements in COOrdinate format>