GCNNormο
- class dgl.transforms.GCNNorm(eweight_name='w')[source]ο
Bases:
BaseTransform
Apply symmetric adjacency normalization to an input graph and save the result edge weights, as described in Semi-Supervised Classification with Graph Convolutional Networks.
For a heterogeneous graph, this only applies to symmetric canonical edge types, whose source and destination node types are identical.
- Parameters:
eweight_name (str, optional) β
edata
name to retrieve and store edge weights. The edge weights are optional.
Example
>>> import dgl >>> import torch >>> from dgl import GCNNorm >>> transform = GCNNorm() >>> g = dgl.graph(([0, 1, 2], [0, 0, 1]))
Case1: Transform an unweighted graph
>>> g = transform(g) >>> print(g.edata['w']) tensor([0.5000, 0.7071, 0.0000])
Case2: Transform a weighted graph
>>> g.edata['w'] = torch.tensor([0.1, 0.2, 0.3]) >>> g = transform(g) >>> print(g.edata['w']) tensor([0.3333, 0.6667, 0.0000])