gammagl.transforms.DropEdge

class DropEdge(p=0.3)

Randomly drop edges, as described in DropEdge: Towards Deep Graph Convolutional Networks on Node Classification paper.

Parameters:

p (float, optional) – Probability of an edge to be dropped.

Example

>>> import numpy as np
>>> from gammagl.data import graph
>>> from gammagl.transforms import DropEdge
>>> import tensorlayerx as tlx
>>> transform = DropEdge()
>>> g = graph.Graph(x=np.random.randn(5, 16), edge_index=tlx.convert_to_tensor([[0, 0, 0], [1, 2, 3]]), edge_attr=tlx.convert_to_tensor([[0,0,0],[1,1,1],[2,2,2]]),num_nodes=5,)
>>> new_g = transform(g)
>>> print(g)
Graph(edge_index=[2, 3], edge_attr=[3, 3], x=[5, 16], num_nodes=5)
>>> print(new_g)
Graph(edge_index=[2, 2], edge_attr=[2, 3], x=[5, 16], num_nodes=5)
>>> from gammagl.data import HeteroGraph
>>> data = HeteroGraph()
>>> num_papers=5
>>> num_paper_features=6
>>> num_authors=7
>>> num_authors_features=8
>>> data['paper'].x = tlx.convert_to_tensor(np.random.uniform((num_papers, num_paper_features)))
>>> data['author'].x = tlx.convert_to_tensor(np.random.uniform((num_authors, num_authors_features)))
>>> data['author', 'writes', 'paper'].edge_index = tlx.convert_to_tensor([[0, 0, 0], [1, 2, 3]])  # [2, num_edges]
>>> data['author', 'writes', 'paper'].edge_attr = tlx.convert_to_tensor([[0, 0, 0], [1, 1, 1],[2, 2, 2]])
>>> data['author', 'writes', 'author'].edge_index = tlx.convert_to_tensor([[1, 2, 3], [2, 3, 1]])
>>> print(data)
HeteroGraph(
  paper={ x=[5, 6] },
  author={ x=[7, 8] },
  (author, writes, paper)={
    edge_index=[2, 3],
    edge_attr=[3, 3]
  },
  (author, writes, author)={ edge_index=[2, 3] }
)
>>> transform = DropEdge()
>>> new_data = transform(data)
>>> print(new_data)
HeteroGraph(
  paper={ x=[5, 6] },
  author={ x=[7, 8] },
  (author, writes, paper)={
    edge_index=[2, 2],
    edge_attr=[2, 3]
  },
  (author, writes, author)={ edge_index=[2, 2] }
)