34 lines
1.0 KiB
Markdown
34 lines
1.0 KiB
Markdown
Here is the an example code for using EiXGNN from the `"EiX-GNN: Concept-level eigencentrality explainer for graph neural
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networks"<https://arxiv.org/abs/2206.03491>`_ paper
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```python
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from torch_geometric.datasets import TUDataset
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dataset = TUDataset(root="/tmp/ENZYMES", name="ENZYMES")
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data = dataset[0]
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import torch.nn.functional as F
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from torch_geometric.nn import GCNConv, global_mean_pool
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class GCN(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = GCNConv(dataset.num_node_features, 20)
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self.conv2 = GCNConv(20, dataset.num_classes)
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def forward(self, data):
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x, edge_index, batch = data.x, data.edge_index, data.batch
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x = self.conv1(x, edge_index)
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x = F.relu(x)
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x = global_mean_pool(x, batch)
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x = F.softmax(x, dim=1)
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return x
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = GCN().to(device)
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model.eval()
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data = dataset[0].to(device)
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explainer = EiXGNN()
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explained = explainer.forward(model, data.x, data.edge_index)
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```
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