@ghaffarian When using GNN-based approaches for graph-level prediction, we first update node representations, then compute graph-level representations out of node-level representations and finally make a prediction. So in your case I guess you did not compute the graph-level representation out of node-level representations. For a most naive case, you can simply take a sum over the node representations as @VoVAllen suggested here. Also see the corresponding section in the tutorial.

# How to Train and Validate a GCN on Several Graphs?

Thanks a lot @mufeili.

It seems I had misunderstood how GCNs work; but now I get it.

I solved it using your suggestion to apply a `dgl.sum_nodes`

and then applying `torch.softmax`

on the output.

If you know about any other better method than a sum I’ll be glad to know about it.

Even a reference would be great to deepen my understanding of the topic.