- The theory (thm 2, cor 1) on the representational power of SMPs is only for simple unlabeled graphs. Is there any similar result for graphs with node and/or edge features ? - The experiments are quite limited. I wish to have seen SMPs in the context of graphs with node and edge features and on standard benchmarks used by GCNs/GIN/GAT models. - how good is SMP at counting cycles ? - is fast SMP less expressive than SMP ? I wish to have seen more discussion on the power of different architectures. - are, in the limit of using sufficiently many layers, all embeddings of node j at each node i becoming equal ? Can this be formally proved ? If this is not true, then isn't it a weakness that each node can learn a potentially different representation of all the nodes in the graph ? - how well is fast SMP perming on the cycle detection task ? ====================== Later edit: I agree with other reviewers' comments on the lack of powerful equivariant baselines and I do believe that the current experimental setup is limited. I am lowering my score as a consequence.
- is fast SMP less expressive than SMP ? I wish to have seen more discussion on the power of different architectures.