Post #240

Simple scalable graph neural networks Michael Bronstein continues a marathon of great blog posts on GML. In a new post he describes their recent work on scaling GNNs to large network. There is a good introduction to sampling-based methods (e.g. SAGE, GraphSAINT, ClusterGCN), which sample a subgraph of a large graph and then train GNN only on a subgraph. Then, he describes that it can be beneficial just precompute r-hop matrices, A^r X, and use MLP on these features. This way, you use topology of your graph and you apply mini-batch training with MLP. What's cool is that the algorithm is already available in pytorch-geometric as a transform, which is implemented via sparseTensor matrix multiplication.