TAGConv¶
-
class
dgl.nn.pytorch.conv.TAGConv(in_feats, out_feats, k=2, bias=True, activation=None)[source]¶ Bases:
torch.nn.modules.module.ModuleTopology Adaptive Graph Convolutional layer from Topology Adaptive Graph Convolutional Networks
\[H^{K} = {\sum}_{k=0}^K (D^{-1/2} A D^{-1/2})^{k} X {\Theta}_{k},\]where \(A\) denotes the adjacency matrix, \(D_{ii} = \sum_{j=0} A_{ij}\) its diagonal degree matrix, \({\Theta}_{k}\) denotes the linear weights to sum the results of different hops together.
- Parameters
in_feats (int) – Input feature size. i.e, the number of dimensions of \(X\).
out_feats (int) – Output feature size. i.e, the number of dimensions of \(H^{K}\).
k (int, optional) – Number of hops \(K\). Default:
2.bias (bool, optional) – If True, adds a learnable bias to the output. Default:
True.activation (callable activation function/layer or None, optional) – If not None, applies an activation function to the updated node features. Default:
None.
-
lin¶ The learnable linear module.
- Type
torch.Module
Example
>>> import dgl >>> import numpy as np >>> import torch as th >>> from dgl.nn import TAGConv >>> >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) >>> feat = th.ones(6, 10) >>> conv = TAGConv(10, 2, k=2) >>> res = conv(g, feat) >>> res tensor([[ 0.5490, -1.6373], [ 0.5490, -1.6373], [ 0.5490, -1.6373], [ 0.5513, -1.8208], [ 0.5215, -1.6044], [ 0.3304, -1.9927]], grad_fn=<AddmmBackward>)
-
forward(graph, feat, edge_weight=None)[source]¶ Compute topology adaptive graph convolution.
- Parameters
graph (DGLGraph) – The graph.
feat (torch.Tensor) – The input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes.
edge_weight (torch.Tensor, optional) – edge_weight to use in the message passing process. This is equivalent to using weighted adjacency matrix in the equation above, and \(\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}\) is based on
dgl.nn.pytorch.conv.graphconv.EdgeWeightNorm.
- Returns
The output feature of shape \((N, D_{out})\) where \(D_{out}\) is size of output feature.
- Return type
torch.Tensor