DropNode¶
-
class
dgl.transforms.
DropNode
(p=0.5)[source]¶ Bases:
dgl.transforms.module.BaseTransform
Randomly drop nodes, as described in Graph Contrastive Learning with Augmentations.
- Parameters
p (float, optional) – Probability of a node to be dropped.
Example
>>> import dgl >>> import torch >>> from dgl import DropNode
>>> transform = DropNode() >>> g = dgl.rand_graph(5, 20) >>> g.ndata['h'] = torch.arange(g.num_nodes()) >>> g.edata['h'] = torch.arange(g.num_edges()) >>> new_g = transform(g) >>> print(new_g) Graph(num_nodes=3, num_edges=7, ndata_schemes={'h': Scheme(shape=(), dtype=torch.int64)} edata_schemes={'h': Scheme(shape=(), dtype=torch.int64)}) >>> print(new_g.ndata['h']) tensor([0, 1, 2]) >>> print(new_g.edata['h']) tensor([0, 6, 14, 5, 17, 3, 11])