Graph Machine Learning
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Everything about graph theory, computer science, machine learning, etc.


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Admins: Sergey Ivanov; Michael Galkin; Chaitanya K. Joshi
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My personal favorite from ICLR 2020. The paper shows on which conditions GNN can compute any function and that the product of depth*width of GNN should be of size ~n in order to compute popular statistics on graphs (e.g. diameter, vertex cover, coloring, etc.).