Unigcn#
UniGCN class.
- class topomodelx.nn.hypergraph.unigcn.UniGCN(in_channels, hidden_channels, n_layers=2, **kwargs)[source]#
Neural network implementation of UniGCN [1] for hypergraph classification.
- Parameters:
- in_channelsint
Dimension of the input features.
- hidden_channelsint
Dimension of the hidden features.
- n_layersint, default = 2
Amount of message passing layers.
- **kwargsoptional
Additional arguments for the inner layers.
References
[1]Huang and Yang. UniGNN: a unified framework for graph and hypergraph neural networks. IJCAI 2021. https://arxiv.org/pdf/2105.00956.pdf
- forward(x_0, incidence_1)[source]#
Forward computation through layers, then linear layer, then global max pooling.
- Parameters:
- x_0torch.Tensor, shape = (n_edges, channels_edge)
Edge features.
- incidence_1torch.Tensor, shape = (n_nodes, n_edges)
Boundary matrix of rank 1.
- Returns:
- x_0torch.Tensor
Output node features.
- x_1torch.Tensor
Output hyperedge features.