



  • $\begingroup$ There are dozens of such methods that you'll find by looking at papers that cite e.g., the first paper you mention which is well-known in the area. But I think you are not asking the right question. I mean, you could always output say a 2-element vector $(|V|,|E|)$, but it won't probably have enough information to be helpful in your ML task. So you need to think harder as to what properties of the graph you want to capture that would be helpful, and go from there. $\endgroup$ – Juho Aug 15 '19 at 16:09
  • $\begingroup$ Each node has a feature associated belonging to $R^d$ space, and each edge a weight associated. I would want to extract a global information that capture, features in nodes, weigh in edges and the relational structure. $\endgroup$ – Roger Figueroa Quintero Aug 16 '19 at 1:13

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