# GINConv¶

class dgl.nn.pytorch.conv.GINConv(apply_func=None, aggregator_type='sum', init_eps=0, learn_eps=False, activation=None)[source]

Bases: torch.nn.modules.module.Module

Graph Isomorphism Network layer from How Powerful are Graph Neural Networks?

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

If a weight tensor on each edge is provided, the weighted graph convolution is defined as:

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

where $$e_{ji}$$ is the weight on the edge from node $$j$$ to node $$i$$. Please make sure that e_{ji} is broadcastable with h_j^{l}.

Parameters
• apply_func (callable activation function/layer or None) – If not None, apply this function to the updated node feature, the $$f_\Theta$$ in the formula, default: None.

• aggregator_type (str) – Aggregator type to use (sum, max or mean), default: ‘sum’.

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

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

• activation (callable activation function/layer or None, optional) – If not None, applies an activation function to the updated node features. Default: None.

Examples

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import GINConv
>>>
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> feat = th.ones(6, 10)
>>> lin = th.nn.Linear(10, 10)
>>> conv = GINConv(lin, 'max')
>>> res = conv(g, feat)
>>> res
tensor([[-0.4821,  0.0207, -0.7665,  0.5721, -0.4682, -0.2134, -0.5236,  1.2855,
0.8843, -0.8764],
[-0.4821,  0.0207, -0.7665,  0.5721, -0.4682, -0.2134, -0.5236,  1.2855,
0.8843, -0.8764],
[-0.4821,  0.0207, -0.7665,  0.5721, -0.4682, -0.2134, -0.5236,  1.2855,
0.8843, -0.8764],
[-0.4821,  0.0207, -0.7665,  0.5721, -0.4682, -0.2134, -0.5236,  1.2855,
0.8843, -0.8764],
[-0.4821,  0.0207, -0.7665,  0.5721, -0.4682, -0.2134, -0.5236,  1.2855,
0.8843, -0.8764],
[-0.1804,  0.0758, -0.5159,  0.3569, -0.1408, -0.1395, -0.2387,  0.7773,

>>> # With activation
>>> from torch.nn.functional import relu
>>> conv = GINConv(lin, 'max', activation=relu)
>>> res = conv(g, feat)
>>> res
tensor([[5.0118, 0.0000, 0.0000, 3.9091, 1.3371, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[5.0118, 0.0000, 0.0000, 3.9091, 1.3371, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[5.0118, 0.0000, 0.0000, 3.9091, 1.3371, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[5.0118, 0.0000, 0.0000, 3.9091, 1.3371, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[5.0118, 0.0000, 0.0000, 3.9091, 1.3371, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[2.5011, 0.0000, 0.0089, 2.0541, 0.8262, 0.0000, 0.0000, 0.1371, 0.0000,

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

Compute Graph Isomorphism Network layer.

Parameters
• graph (DGLGraph) – The graph.

• feat (torch.Tensor or pair of torch.Tensor) – If a torch.Tensor is given, 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 dimensionality requirement of apply_func.

• edge_weight (torch.Tensor, optional) – Optional tensor on the edge. If given, the convolution will weight with regard to the message.

Returns

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

Return type

torch.Tensor