dgl.DGLGraph.out_degrees

DGLGraph.out_degrees(u='__ALL__', etype=None)[source]

Return the out-degree(s) of the given nodes.

It computes the out-degree(s) w.r.t. to the edges of the given edge type.

Parameters:

u (node IDs) –
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 graph’s.
- iterable[int]: Each element is a node ID.
If not given, return the in-degrees of all the nodes.
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:

The out-degree(s) of the node(s) in a Tensor. The i-th element is the out-degree of the i-th input node. If v is an int, return an int too.

Return type:

int or 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, 1, 2, 3])))

Query for all nodes.

>>> g.out_degrees()
tensor([2, 2, 0, 0])

Query for nodes 1 and 2.

>>> g.out_degrees(torch.tensor([1, 2]))
tensor([2, 0])

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.out_degrees(torch.tensor([1, 0]), etype='follows')
tensor([1, 1])