torch_geometric.nn.conv.PANConv
- class PANConv(in_channels: int, out_channels: int, filter_size: int, **kwargs)[source]
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:\[\mathbf{M} = \mathbf{Z}^{-1/2} \sum_{n=0}^L e^{-\frac{E(n)}{T}} \mathbf{A}^n \mathbf{Z}^{-1/2}\]- Parameters:
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
.
- Shapes:
input: node features \((|\mathcal{V}|, F_{in})\), edge indices \((2, |\mathcal{E}|)\),
output: node features \((|\mathcal{V}|, F_{out})\)