torch_geometric.nn.conv.HypergraphConv
- class HypergraphConv(in_channels: int, out_channels: int, use_attention: bool = False, attention_mode: str = 'node', heads: int = 1, concat: bool = True, negative_slope: float = 0.2, dropout: float = 0, bias: bool = True, **kwargs)[source]
Bases:
MessagePassing
The hypergraph convolutional operator from the “Hypergraph Convolution and Hypergraph Attention” paper.
\[\mathbf{X}^{\prime} = \mathbf{D}^{-1} \mathbf{H} \mathbf{W} \mathbf{B}^{-1} \mathbf{H}^{\top} \mathbf{X} \mathbf{\Theta}\]where \(\mathbf{H} \in {\{ 0, 1 \}}^{N \times M}\) is the incidence matrix, \(\mathbf{W} \in \mathbb{R}^M\) is the diagonal hyperedge weight matrix, and \(\mathbf{D}\) and \(\mathbf{B}\) are the corresponding degree matrices.
For example, in the hypergraph scenario \(\mathcal{G} = (\mathcal{V}, \mathcal{E})\) with \(\mathcal{V} = \{ 0, 1, 2, 3 \}\) and \(\mathcal{E} = \{ \{ 0, 1, 2 \}, \{ 1, 2, 3 \} \}\), the
hyperedge_index
is represented as:hyperedge_index = torch.tensor([ [0, 1, 2, 1, 2, 3], [0, 0, 0, 1, 1, 1], ])
- 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.
use_attention (bool, optional) – If set to
True
, attention will be added to this layer. (default:False
)attention_mode (str, optional) – The mode on how to compute attention. If set to
"node"
, will compute attention scores of nodes within all nodes belonging to the same hyperedge. If set to"edge"
, will compute attention scores of nodes across all edges holding this node belongs to. (default:"node"
)heads (int, optional) – Number of multi-head-attentions. (default:
1
)concat (bool, optional) – If set to
False
, the multi-head attentions are averaged instead of concatenated. (default:True
)negative_slope (float, optional) – LeakyReLU angle of the negative slope. (default:
0.2
)dropout (float, optional) – Dropout probability of the normalized attention coefficients which exposes each node to a stochastically sampled neighborhood during training. (default:
0
)bias (bool, optional) – If set to
False
, the layer will not learn an additive bias. (default:True
)**kwargs (optional) – Additional arguments of
torch_geometric.nn.conv.MessagePassing
.
- Shapes:
input: node features \((|\mathcal{V}|, F_{in})\), hyperedge indices \((|\mathcal{V}|, |\mathcal{E}|)\), hyperedge weights \((|\mathcal{E}|)\) (optional) hyperedge features \((|\mathcal{E}|, D)\) (optional)
output: node features \((|\mathcal{V}|, F_{out})\)
- forward(x: Tensor, hyperedge_index: Tensor, hyperedge_weight: Optional[Tensor] = None, hyperedge_attr: Optional[Tensor] = None, num_edges: Optional[int] = None) Tensor [source]
Runs the forward pass of the module.
- Parameters:
x (torch.Tensor) – Node feature matrix \(\mathbf{X} \in \mathbb{R}^{N \times F}\).
hyperedge_index (torch.Tensor) – The hyperedge indices, i.e. the sparse incidence matrix \(\mathbf{H} \in {\{ 0, 1 \}}^{N \times M}\) mapping from nodes to edges.
hyperedge_weight (torch.Tensor, optional) – Hyperedge weights \(\mathbf{W} \in \mathbb{R}^M\). (default:
None
)hyperedge_attr (torch.Tensor, optional) – Hyperedge feature matrix in \(\mathbb{R}^{M \times F}\). These features only need to get passed in case
use_attention=True
. (default:None
)num_edges (int, optional) – The number of edges \(M\). (default:
None
)
- Return type: