Node Classification for Multiple Graphs

[ghost]

I have multiple graphs for node classification task. All the examples I've seen so far was for graph classification(or there is just one graph for node classification task). Although I've seen building block diagonal adjacency matrix, I'm not sure if it is for graph classification or node. Also I didn't understand whether should I create a block diagonal matrix with feature matrix and labels or not.

Let's suppose I have 20 different graphs(with different number of nodes, edges, features). And each node of every graph is labeled.
All the nodes in first 10 graphs are for training, all the nodes of the next 5 graphs are for val, and all the nodes of last 5 graphs are for test, and what I try to do is predicting labels of the nodes for the graphs in test-set. How can I input multiple graphs into GCN with these conditions for node classification task(not for graph classification).? If the solution is diagonal adj. matrix, should I do the same for labels and feature matrix?

[qillbel]

Hey @mulaixi, I have a similar problem. Have you discovered the answer?

Thanks in advance

[yvonneywu]

Hi @qillbel and @mulaixi , also met this problem!!
Did you found something solve this problem?
Thanks!

[qillbel]

hi @dengerrrr ,

I brought the discussion to https://discuss.dgl.ai/ for the implementation of GCN.

Thanks

[tanjia123456]

@qillbel @dengerrrr @mulaixi
hello, I also meet such problem, do you know how to solve it? thank you very much