dgl.DGLGraph.has_edges_between¶
-
DGLGraph.
has_edges_between
(u, v, etype=None)¶ Return whether the graph contains the given edges.
- Parameters
u (node IDs) –
The source node IDs of the edges. The allowed formats are:
int
: A single node.Int Tensor: Each element is a node ID. The tensor must have the same device type and ID data type as the graph’s.
iterable[int]: Each element is a node ID.
v (node IDs) –
The destination node IDs of the edges. The allowed formats are:
int
: A single node.Int Tensor: Each element is a node ID. The tensor must have the same device type and ID data type as the graph’s.
iterable[int]: Each element is a node ID.
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.
- Returns
A tensor of bool flags where each element is True if the node is in the graph. If the input is a single node, return one bool value.
- Return type
bool or bool Tensor
Examples
The following example uses PyTorch backend.
>>> import dgl >>> import torch
Create a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 0, 1, 1]), torch.tensor([1, 0, 2, 3])))
Query for the edges.
>>> g.has_edges_between(1, 2) True >>> g.has_edges_between(torch.tensor([1, 2]), torch.tensor([2, 3])) tensor([ True, False])
If the graph has multiple edge types, one need to specify the edge type.
>>> g = dgl.heterograph({ ... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])), ... ('user', 'follows', 'game'): (torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])), ... ('user', 'plays', 'game'): (torch.tensor([1, 3]), torch.tensor([2, 3])) ... }) >>> g.has_edges_between(torch.tensor([1, 2]), torch.tensor([2, 3]), 'plays') tensor([ True, False])
Use a canonical edge type instead when there is ambiguity for an edge type.
>>> g.has_edges_between(torch.tensor([1, 2]), torch.tensor([2, 3]), ... ('user', 'follows', 'user')) tensor([ True, False]) >>> g.has_edges_between(torch.tensor([1, 2]), torch.tensor([2, 3]), ... ('user', 'follows', 'game')) tensor([True, True])