gammagl.utils.add_self_loops

class add_self_loops(edge_index, edge_attr=None, n_loops=1, fill_value: float | str | None = None, num_nodes: int | None = None)[source]

Adds a self-loop \((i,i) \in \mathcal{E}\) to every node \(i \in \mathcal{V}\) in the graph given by edge_index. In case the graph is weighted or has multi-dimensional edge features (edge_attr != None), edge features of self-loops will be added according to fill_value.

>>> from gammagl.data import Graph
>>> from gammagl.utils.loop import add_self_loops
>>> import numpy
>>> edge_index = tlx.constant([[0, 0, 0], [1, 2, 3]])
array([[0, 0, 0],
        [1, 2, 3]])
>>> edge_index, _ = add_self_loops(edge_index)
array([[0, 0, 0, 0, 1, 2, 3],
       [1, 2, 3, 0, 1, 2, 3]])
Parameters:

  • edge_index (tensor) – The edge indices.

  • n_loops (int) – the number of loops

  • edge_attr (tensor, optional) – Edge weights or multi-dimensional edge features. (default: None)

  • fill_value (float, tensor, str, optional) – The way to generate edge features of self-loops (in case edge_attr != None). If given as float or torch.Tensor, edge features of self-loops will be directly given by fill_value. If given as str, edge features of self-loops are computed by aggregating all features of edges that point to the specific node, according to a reduce operation. ("add", "mean", "min", "max", "mul"). (default: 1.)

  • num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type:

LongTensor, Tensor