Post #222

Do we need deep graph neural networks? A very frequent research question is discussed in a new blog post of Michael Bronstein. The problem with deep GNN is called over-smoothing and is related to the fact that with more layers GNN tends to produce embeddings that are equal across all nodes. This problem started with AAAI'20 paper and now received a lot of attention (I'd say this is the 2nd most popular theoretical question about GNN after expressiveness), proposing different methods to tackle over-smoothing. It seems that if the graph/node labels depend on high-order information such as graphlets then the depth is necessary; however, encountering such data sets in real life may not be common,