Post #70

Benchmarking Graph Neural Networks This is a recent submission to ICML that compares existing GNN on multiple data sets. There was already an accepted paper at ICLR 2020 so the two works largely overlap (and I guess that would decrease the chances for ICML submissions to be accepted). Good points: * The authors propose 6 new data sets with tens of thousands graphs. * They provide learned lessons: 1. GNNs are better than MLP for their proposed data sets. 2. Adding weights for each edge helps. 3. Residual connections and normalization helps. Bad points: * The data sets are still small in terms of the size of the graphs, ~100 of nodes. * No metrics beyond accuracy.