Hi, DGL is a graceful tool for graph learning.
However, it seems that there is no tutorial or examples in DGL to combine the existing models easily?
G.ndata['feature'] = features
note the node features are derived from other modules (e.g., a CNN/RNN feature extractor), so in this way, would the whole pytorch computation graph be complete for backpropagation?
Or, if we have obtained the results of GCN Layers, such as:
x = self.gcn1(g, features) x = self.gcn2(g, x)
If I continue to conduct other operations on
x or part columns of
x, can the computation graph be complete?