Bases: MessagePassing
The topology adaptive graph convolutional networks operator from the “Topology Adaptive Graph Convolutional Networks” paper.
\[\mathbf{X}^{\prime} = \sum_{k=0}^K \left( \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2} \right)^k \mathbf{X} \mathbf{W}_{k},\]
where \(\mathbf{A}\) denotes the adjacency matrix and \(D_{ii} = \sum_{j=0} A_{ij}\) its diagonal degree matrix. The adjacency matrix can include other values than 1 representing edge weights via the optional edge_weight tensor.
in_channels (int) – Size of each input sample, or -1 to derive the size from the first input(s) to the forward method.
out_channels (int) – Size of each output sample.
K (int, optional) – Number of hops \(K\). (default: 3)
bias (bool, optional) – If set to False, the layer will not learn an additive bias. (default: True)
normalize (bool, optional) – Whether to apply symmetric normalization. (default: True)
**kwargs (optional) – Additional arguments of torch_geometric.nn.conv.MessagePassing.
input: node_features \((|\mathcal{V}|, F_{in})\), edge_index \((2, |\mathcal{E}|)\), edge_weights \((|\mathcal{E}|)\) (optional)
output: node features \((|\mathcal{V}|, F_{out})\)
Runs the forward pass of the module.
Resets all learnable parameters of the module.
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