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GCN utilizes edge attributes. previous regularizing techniques consider node similarity, but GCN considers node features. its in the loss function (eq. 8 ). GCN can be used for directed graphs by constructing it as a bipartite graph. See https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/SDG.pdf for using bipartite graphs for learning on directed graphs
I am new to the GCN research and was trying to differentiate which GCN is suitable for different problems. Here is what I think:
I might be wrong but I really appreciate if you (@tkipf ) could clear this doubt for me. Thanks
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