dgl.DGLGraph.batch_num_edgesο

DGLGraph.batch_num_edges(etype=None)[source]ο

Return the number of edges for each graph in the batch with the specified edge type.

Parameters:

etype (str or tuple of str, optional) β The edge type for query, which can be an edge type (str) or a canonical edge type (3-tuple of str). When an edge type appears in multiple canonical edge types, one must use a canonical edge type. If the graph has multiple edge types, one must specify the argument. Otherwise, it can be omitted.

Returns:

The number of edges with the specified type for each graph in the batch. The i-th element of it is the number of edges with the specified type for the i-th graph. If the graph is not a batched one, it will return a list of length 1 that holds the number of edges in the graph.

Return type:

Tensor

Examples

The following example uses PyTorch backend.

```>>> import dgl
>>> import torch
```

Query for homogeneous graphs.

```>>> g1 = dgl.graph((torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])))
>>> g1.batch_num_edges()
tensor([3])
>>> g2 = dgl.graph((torch.tensor([0, 0, 0, 1]), torch.tensor([0, 1, 2, 0])))
>>> bg = dgl.batch([g1, g2])
>>> bg.batch_num_edges()
tensor([3, 4])
```

Query for heterogeneous graphs.

```>>> hg1 = dgl.heterograph({
...       ('user', 'plays', 'game') : (torch.tensor([0, 1]), torch.tensor([0, 0]))})
>>> hg2 = dgl.heterograph({
...       ('user', 'plays', 'game') : (torch.tensor([0, 0]), torch.tensor([1, 0]))})
>>> bg = dgl.batch([hg1, hg2])
>>> bg.batch_num_edges('plays')
tensor([2, 2])
```