# GatedGraphConv¶

class dgl.nn.pytorch.conv.GatedGraphConv(in_feats, out_feats, n_steps, n_etypes, bias=True)[source]

Bases: torch.nn.modules.module.Module

Gated Graph Convolution layer from Gated Graph Sequence Neural Networks

\begin{align}\begin{aligned}h_{i}^{0} &= [ x_i \| \mathbf{0} ]\\a_{i}^{t} &= \sum_{j\in\mathcal{N}(i)} W_{e_{ij}} h_{j}^{t}\\h_{i}^{t+1} &= \mathrm{GRU}(a_{i}^{t}, h_{i}^{t})\end{aligned}\end{align}
Parameters
• in_feats (int) – Input feature size; i.e, the number of dimensions of $$x_i$$.

• out_feats (int) – Output feature size; i.e., the number of dimensions of $$h_i^{(t+1)}$$.

• n_steps (int) – Number of recurrent steps; i.e, the $$t$$ in the above formula.

• n_etypes (int) – Number of edge types.

• bias (bool) – If True, adds a learnable bias to the output. Default: True.

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import GatedGraphConv
>>>
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> feat = th.ones(6, 10)
>>> conv = GatedGraphConv(10, 10, 2, 3)
>>> etype = th.tensor([0,1,2,0,1,2])
>>> res = conv(g, feat, etype)
>>> res
tensor([[ 0.4652,  0.4458,  0.5169,  0.4126,  0.4847,  0.2303,  0.2757,  0.7721,
0.0523,  0.0857],
[ 0.0832,  0.1388, -0.5643,  0.7053, -0.2524, -0.3847,  0.7587,  0.8245,
0.9315,  0.4063],
[ 0.6340,  0.4096,  0.7692,  0.2125,  0.2106,  0.4542, -0.0580,  0.3364,
-0.1376,  0.4948],
[ 0.5551,  0.7946,  0.6220,  0.8058,  0.5711,  0.3063, -0.5454,  0.2272,
-0.6931, -0.1607],
[ 0.2644,  0.2469, -0.6143,  0.6008, -0.1516, -0.3781,  0.5878,  0.7993,
0.9241,  0.1835],
[ 0.6393,  0.3447,  0.3893,  0.4279,  0.3342,  0.3809,  0.0406,  0.5030,

forward(graph, feat, etypes=None)[source]

Compute Gated Graph Convolution layer.

Parameters
• graph (DGLGraph) – The graph.

• feat (torch.Tensor) – The input feature of shape $$(N, D_{in})$$ where $$N$$ is the number of nodes of the graph and $$D_{in}$$ is the input feature size.

• etypes (torch.LongTensor, or None) – The edge type tensor of shape $$(E,)$$ where $$E$$ is the number of edges of the graph. When there’s only one edge type, this argument can be skipped

Returns

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

Return type

torch.Tensor

reset_parameters()[source]

Reinitialize learnable parameters.

Note

The model parameters are initialized using Glorot uniform initialization and the bias is initialized to be zero.