TAGConv

class dgl.nn.pytorch.conv.TAGConv(in_feats, out_feats, k=2, bias=True, activation=None)

Bases: Module

Topology Adaptive Graph Convolutional layer from Topology Adaptive Graph Convolutional Networks

$$H^K={\sum }_{k=0}^K(D^{-1/2}A{D}^{-1/2}{)}^{k}X{\mathrm{\Theta }}_k,$$

where $A$ denotes the adjacency matrix, $D_{ii}=\sum _{j=0}{A}_{ij}$ its diagonal degree matrix, ${\mathrm{\Theta }}_k$ 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)

Description

Compute topology adaptive graph convolution.

param graph:

The graph.

type graph:

DGLGraph

param feat:

The input feature of shape $(N,D_{in})$ where $D_{in}$ is size of input feature, $N$ is the number of nodes.

type feat:

torch.Tensor

param edge_weight:

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.

type edge_weight:

torch.Tensor, optional

returns:

The output feature of shape $(N,D_{out})$ where $D_{out}$ is size of output feature.

rtype:

torch.Tensor

reset_parameters()

Description

Reinitialize learnable parameters.

Note

The model parameters are initialized using Glorot uniform initialization.