This is very interesting work. Regarding: "However, graphs are inherently combinatorial structures made of discrete parts like nodes and edges, while many comm

This is very interesting work. Regarding: "However, graphs are inherently combinatorial structures made of discrete parts like nodes and edges, while many common ML methods, like neural networks, favor continuous structures, in particular vector representations." I apologize in advance for a bit of self-promotion, but I would like to point out my own approach, which instead favors discrete ML methods to discover symbolic generators of networks. That is to say, small programs that are capable of generating synthetic networks with similar topological and other characteristics to some empirically observed one. If you happen to be interested: https://www.nature.com/articles/srep06284 https://www.nature.com/articles/srep06284 http://www.telmomenezes.net/2014/09/using-evolutionary-computation-to-explain-network-growth/ http://www.telmomenezes.net/2014/09/using-evolutionary-compu... I am not saying that my approach is better, it depends on the goal. On the contrary, I am increasingly a believer in hybrid (symbolic-statistic) methods.