dgl.DGLGraph.in_edgesΒΆ
-
DGLGraph.
in_edges
(v, form='uv', etype=None)[source]ΒΆ Return the incoming edges of the given nodes.
- Parameters
v (node ID(s)) β
The node IDs. 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 graphβs.
iterable[int]: Each element is a node ID.
form (str, optional) β
The result format, which can be one of the following:
'eid'
: The returned result is a 1D tensor \(EID\), representing the IDs of all edges.'uv'
(default): The returned result is a 2-tuple of 1D tensors \((U, V)\), representing the source and destination nodes of all edges. For each \(i\), \((U[i], V[i])\) forms an edge.'all'
: The returned result is a 3-tuple of 1D tensors \((U, V, EID)\), representing the source nodes, destination nodes and IDs of all edges. For each \(i\), \((U[i], V[i])\) forms an edge with ID \(EID[i]\).
etype (str or (str, str, str), optional) β
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
All incoming edges of the nodes with the specified type. For a description of the returned result, see the description of
form
.- Return type
Tensor or (Tensor, Tensor) or (Tensor, Tensor, 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 nodes 1 and 0.
>>> g.in_edges(torch.tensor([1, 0])) (tensor([0, 0]), tensor([1, 0]))
Specify a different value for
form
.>>> g.in_edges(torch.tensor([1, 0]), form='all') (tensor([0, 0]), tensor([1, 0]), tensor([0, 1]))
For a graph of multiple edge types, it is required to specify the edge type in query.
>>> hg = dgl.heterograph({ ... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])), ... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6])) ... }) >>> hg.in_edges(torch.tensor([1, 0]), etype='follows') (tensor([0]), tensor([1]))