class GENConv(in_channels: Union[int, Tuple[int, int]], out_channels: int, aggr: Optional[Union[str, List[str], Aggregation]] = 'softmax', t: float = 1.0, learn_t: bool = False, p: float = 1.0, learn_p: bool = False, msg_norm: bool = False, learn_msg_scale: bool = False, norm: str = 'batch', num_layers: int = 2, expansion: int = 2, eps: float = 1e-07, bias: bool = False, edge_dim: Optional[int] = None, **kwargs)[source]

Bases: MessagePassing

The GENeralized Graph Convolution (GENConv) from the “DeeperGCN: All You Need to Train Deeper GCNs” paper.

GENConv supports both \(\textrm{softmax}\) (see SoftmaxAggregation) and \(\textrm{powermean}\) (see PowerMeanAggregation) aggregation. Its message construction is given by:

\[\mathbf{x}_i^{\prime} = \mathrm{MLP} \left( \mathbf{x}_i + \mathrm{AGG} \left( \left\{ \mathrm{ReLU} \left( \mathbf{x}_j + \mathbf{e_{ji}} \right) +\epsilon : j \in \mathcal{N}(i) \right\} \right) \right)\]


For an example of using GENConv, see examples/

  • in_channels (int or tuple) – Size of each input sample, or -1 to derive the size from the first input(s) to the forward method. A tuple corresponds to the sizes of source and target dimensionalities.

  • out_channels (int) – Size of each output sample.

  • aggr (str or Aggregation, optional) – The aggregation scheme to use. Any aggregation of torch_geometric.nn.aggr can be used, ("softmax", "powermean", "add", "mean", max). (default: "softmax")

  • t (float, optional) – Initial inverse temperature for softmax aggregation. (default: 1.0)

  • learn_t (bool, optional) – If set to True, will learn the value t for softmax aggregation dynamically. (default: False)

  • p (float, optional) – Initial power for power mean aggregation. (default: 1.0)

  • learn_p (bool, optional) – If set to True, will learn the value p for power mean aggregation dynamically. (default: False)

  • msg_norm (bool, optional) – If set to True, will use message normalization. (default: False)

  • learn_msg_scale (bool, optional) – If set to True, will learn the scaling factor of message normalization. (default: False)

  • norm (str, optional) – Norm layer of MLP layers ("batch", "layer", "instance") (default: batch)

  • num_layers (int, optional) – The number of MLP layers. (default: 2)

  • expansion (int, optional) – The expansion factor of hidden channels in MLP layers. (default: 2)

  • eps (float, optional) – The epsilon value of the message construction function. (default: 1e-7)

  • bias (bool, optional) – If set to False, the layer will not learn an additive bias. (default: True)

  • edge_dim (int, optional) – Edge feature dimensionality. If set to None, Edge feature dimensionality is expected to match the out_channels. Other-wise, edge features are linearly transformed to match out_channels of node feature dimensionality. (default: None)

  • **kwargs (optional) – Additional arguments of torch_geometric.nn.conv.GenMessagePassing.

  • input: node features \((|\mathcal{V}|, F_{in})\) or \(((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))\) if bipartite, edge indices \((2, |\mathcal{E}|)\), edge attributes \((|\mathcal{E}|, D)\) (optional)

  • output: node features \((|\mathcal{V}|, F_{out})\) or \((|\mathcal{V}_t|, F_{out})\) if bipartite

forward(x: Union[Tensor, Tuple[Tensor, Optional[Tensor]]], edge_index: Union[Tensor, SparseTensor], edge_attr: Optional[Tensor] = None, size: Optional[Tuple[int, int]] = None) Tensor[source]

Runs the forward pass of the module.


Resets all learnable parameters of the module.