torch_geometric.nn.conv.LEConv

class LEConv(in_channels: Union[int, Tuple[int, int]], out_channels: int, bias: bool = True, **kwargs)[source]

Bases: MessagePassing

The local extremum graph neural network operator from the “ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations” paper.

LEConv finds the importance of nodes with respect to their neighbors using the difference operator:

\[\mathbf{x}^{\prime}_i = \mathbf{x}_i \cdot \mathbf{\Theta}_1 + \sum_{j \in \mathcal{N}(i)} e_{j,i} \cdot (\mathbf{\Theta}_2 \mathbf{x}_i - \mathbf{\Theta}_3 \mathbf{x}_j)\]

where \(e_{j,i}\) denotes the edge weight from source node j to target node i (default: 1)

Parameters:

  • 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.

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

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

Shapes:

  • 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 features \((|\mathcal{E}|, D)\) (optional)

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

forward(x: Union[Tensor, Tuple[Tensor, Tensor]], edge_index: Union[Tensor, SparseTensor], edge_weight: Optional[Tensor] = None) → Tensor[source]

Runs the forward pass of the module.

reset_parameters()[source]

Resets all learnable parameters of the module.