gammagl.models.DGCNN

class DGCNN(feature_dim, hidden_dim, num_layers, gcn_type='gcn', k=0.6, train_dataset=None, dropout=0.5, name=None)[source]

DGCNN proposed in “An End-to-End Deep Learning Architecture for Graph Classification” paper.

Parameters:

  • feature_dim (int) – input feature dimension.

  • hidden_dim (int) – hidden dimension.

  • num_layers (int) – number of layers.

  • gcn_type (str) – convolution layer type.

  • k (int or float) – The number of nodes to hold for each graph in SortPooling.

  • train_dataset (dataset) – train dataset to extract minimum number of nodes to generate k.

  • dropout (float) – dropout rate.

  • name (str) – model name.

forward(x, edge_index, batch)[source]