Post #213

Beyond Weisfeiler-Lehman: approximate isomorphisms and metric embeddings Last post in a series of posts by M. Bronstein about how we can reformulate the framework of designing GNNs. Most popular approach currently is to show that GNN can be equivalent to WL algorithm, which in turn implies that the algorithm can detect isomorphism of most graphs. However, this is not very valuable in practice, as there are very few graphs. Instead, it would be great to have GNN that preserve some kind of distance (e.g. edit distance) between the graphs in euclidean space, up to some error. This can be seen as a generalization of the current framework, when we care only about the case when the two graphs are isomorphic.