Bases: MessagePassing
The path integral based convolutional operator from the “Path Integral Based Convolution and Pooling for Graph Neural Networks” paper.
\[\mathbf{X}^{\prime} = \mathbf{M} \mathbf{X} \mathbf{W}\]
where \(\mathbf{M}\) denotes the normalized and learned maximal entropy transition (MET) matrix that includes neighbors up to filter_size hops:
\[\mathbf{M} = \mathbf{Z}^{-1/2} \sum_{n=0}^L e^{-\frac{E(n)}{T}} \mathbf{A}^n \mathbf{Z}^{-1/2}\]
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.
filter_size (int) – The filter size \(L\).
**kwargs (optional) – Additional arguments of torch_geometric.nn.conv.MessagePassing.
input: node features \((|\mathcal{V}|, F_{in})\), edge indices \((2, |\mathcal{E}|)\),
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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