GNNExplainer with minibatch training

Questions
#1

Hi @mufeili, does GNNExplainer work with minibatch training?

#2

GNNExplainer can be applied to any trained GNN model. However, you need to satisfy the requirement of model in the doc.

#3

Can you elaborate?

For example I have a model using minibatch.

class TwoLayerGCN(nn.Module):
    def __init__(self, in_features, hidden_features, out_features):
        super().__init__()
        self.conv1 = dgl.nn.GraphConv(in_features, hidden_features)
        self.conv2 = dgl.nn.GraphConv(hidden_features, out_features)

    def forward(self, blocks, x):
        x = F.relu(self.conv1(blocks[0], x))
        x = F.relu(self.conv2(blocks[1], x))
        return x

If I want to apply GNNExplainer to this model, I need to convert the blocks to DGLGraph? For example, the model has two blocks, so I need to merge the two blocks into a graph?

class NewTwoLayerGCN(nn.Module):
    def __init__(self, in_features, hidden_features, out_features):
        super().__init__()
        self.conv1 = dglnn.GraphConv(in_features, hidden_features)
        self.conv2 = dglnn.GraphConv(hidden_features, out_features)

    def forward(self, graph, x):
        x = F.relu(self.conv1(graph, x))
        x = F.relu(self.conv2(graph, x))
        return x

Is this the right way? Thanks for your reply. @mufeili

#4

You can have two functions for NewTwoLayerGCN. Let’s say a forward_block function that takes blocks and you use during training and a forward function that takes a DGLGraph and you use for explanation.

#5

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