RandomWalkPE¶

class dgl.transforms.RandomWalkPE(k, feat_name='PE', eweight_name=None)[source]¶

Bases: dgl.transforms.module.BaseTransform

Random Walk Positional Encoding, as introduced in Graph Neural Networks with Learnable Structural and Positional Representations

This module only works for homogeneous graphs.

Parameters

k (int) – Number of random walk steps. The paper found the best value to be 16 and 20 for two experiments.
feat_name (str, optional) – Name to store the computed positional encodings in ndata.
eweight_name (str, optional) – Name to retrieve the edge weights. Default: None, not using the edge weights.

Example

>>> import dgl
>>> from dgl import RandomWalkPE

>>> transform = RandomWalkPE(k=2)
>>> g = dgl.graph(([0, 1, 1], [1, 1, 0]))
>>> g = transform(g)
>>> print(g.ndata['PE'])
tensor([[0.0000, 0.5000],
        [0.5000, 0.7500]])