class WLConvContinuous(**kwargs)[source]

Bases: MessagePassing

The Weisfeiler Lehman operator from the “Wasserstein Weisfeiler-Lehman Graph Kernels” paper.

Refinement is done though a degree-scaled mean aggregation and works on nodes with continuous attributes:

\[\mathbf{x}^{\prime}_i = \frac{1}{2}\big(\mathbf{x}_i + \frac{1}{\textrm{deg}(i)} \sum_{j \in \mathcal{N}(i)} e_{j,i} \cdot \mathbf{x}_j \big)\]

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


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

  • input: node features \((|\mathcal{V}|, F)\) or \(((|\mathcal{V_s}|, F), (|\mathcal{V_t}|, F))\) if bipartite, edge indices \((2, |\mathcal{E}|)\), edge weights \((|\mathcal{E}|)\) (optional)

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

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

Runs the forward pass of the module.

reset_parameters() None

Resets all learnable parameters of the module.