DGLGraph.filter_edges(predicate, edges='__ALL__', etype=None)[source]

Return the IDs of the edges with the given edge type that satisfy the given predicate.

  • predicate (callable) – A function of signature func(edges) -> Tensor. edges are dgl.EdgeBatch objects. Its output tensor should be a 1D boolean tensor with each element indicating whether the corresponding edge in the batch satisfies the predicate.

  • edges (edges) –

    The edges to send and receive messages on. The allowed input formats are:

    • int: A single edge ID.

    • Int Tensor: Each element is an edge ID. The tensor must have the same device type and ID data type as the graph’s.

    • iterable[int]: Each element is an edge ID.

    • (Tensor, Tensor): The node-tensors format where the i-th elements of the two tensors specify an edge.

    • (iterable[int], iterable[int]): Similar to the node-tensors format but stores edge endpoints in python iterables.

    By default, it considers all the edges.

  • etype (str or (str, str, str), optional) –

    The type name 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.


A 1D tensor that contains the ID(s) of the edge(s) that satisfy the predicate.

Return type:



The following example uses PyTorch backend.

>>> import dgl
>>> import torch

Define a predicate function.

>>> def edges_with_feature_one(edges):
...     # Whether an edge has feature 1
...     return (edges.data['h'] == 1.).squeeze(1)

Filter edges for a homogeneous graph.

>>> g = dgl.graph((torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])))
>>> g.edata['h'] = torch.tensor([[0.], [1.], [1.]])
>>> print(g.filter_edges(edges_with_feature_one))
tensor([1, 2])

Filter on edges with IDs 0 and 1

>>> print(g.filter_edges(edges_with_feature_one, edges=torch.tensor([0, 1])))

Filter edges for a heterogeneous graph.

>>> g = dgl.heterograph({
...     ('user', 'plays', 'game'): (torch.tensor([0, 1, 1, 2]),
...                                 torch.tensor([0, 0, 1, 1])),
...     ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2]))})
>>> g.edges['plays'].data['h'] = torch.tensor([[0.], [1.], [1.], [0.]])
>>> # Filter for 'plays' nodes
>>> print(g.filter_edges(edges_with_feature_one, etype='plays'))
tensor([1, 2])