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I'm new in Graph-Embedding and GCN(Graph/Geometric Convolution Network).

I'm confused and not very much sure about "How training works in GCN"?

As per my understanding, GCN training data will be in the form of "Adjacency Matrix" + "Degree Matrix" + Features of nodes(vertex). Using the aggregate of these metrics, we do GCN training. Kindly confirm?

If my understanding is correct, then I believe that even for Graph-Embedding, we use similar type of Input data(as mentioned above) for generating vector representation? Again if it correct, why GCN is better than Graph Embedding?

Or it's incorrect to co-relate both these terms?

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