GINEConvο

class dgl.nn.pytorch.conv.GINEConv(apply_func=None, init_eps=0, learn_eps=False)[source]ο

Bases: Module

Graph Isomorphism Network with Edge Features, introduced by Strategies for Pre-training Graph Neural Networks

$h_i^{(l+1)} = f_\Theta \left((1 + \epsilon) h_i^{l} + \sum_{j\in\mathcal{N}(i)}\mathrm{ReLU}(h_j^{l} + e_{j,i}^{l})\right)$

where $$e_{j,i}^{l}$$ is the edge feature.

Parameters:
• apply_func (callable module or None) β The $$f_\Theta$$ in the formula. If not None, it will be applied to the updated node features. The default value is None.

• init_eps (float, optional) β Initial $$\epsilon$$ value, default: 0.

• learn_eps (bool, optional) β If True, $$\epsilon$$ will be a learnable parameter. Default: False.

Examples

>>> import dgl
>>> import torch
>>> import torch.nn as nn
>>> from dgl.nn import GINEConv
>>> g = dgl.graph(([0, 1, 2], [1, 1, 3]))
>>> in_feats = 10
>>> out_feats = 20
>>> nfeat = torch.randn(g.num_nodes(), in_feats)
>>> efeat = torch.randn(g.num_edges(), in_feats)
>>> conv = GINEConv(nn.Linear(in_feats, out_feats))
>>> res = conv(g, nfeat, efeat)
>>> print(res.shape)
torch.Size([4, 20])
forward(graph, node_feat, edge_feat)[source]ο

Forward computation.

Parameters:
• graph (DGLGraph) β The graph.

• node_feat (torch.Tensor or pair of torch.Tensor) β If a torch.Tensor is given, it is the input feature of shape $$(N, D_{in})$$ where $$D_{in}$$ is size of input feature, $$N$$ is the number of nodes. If a pair of torch.Tensor is given, the pair must contain two tensors of shape $$(N_{in}, D_{in})$$ and $$(N_{out}, D_{in})$$. If apply_func is not None, $$D_{in}$$ should fit the input feature size requirement of apply_func.

• edge_feat (torch.Tensor) β Edge feature. It is a tensor of shape $$(E, D_{in})$$ where $$E$$ is the number of edges.

Returns:

The output feature of shape $$(N, D_{out})$$ where $$D_{out}$$ is the output feature size of apply_func. If apply_func is None, $$D_{out}$$ should be the same as $$D_{in}$$.

Return type:

torch.Tensor