Review for NeurIPS paper: Principal Neighbourhood Aggregation for Graph Nets

Weaknesses: Methodological: The work here places importance on topology/structure. For example, the message scaling is dependent on node degree. Thus this method is apt for applications where the structure is paramount, e.g. one such application mentioned is reasoning about social networks where the degree of the nodes/users provides a lot of information about that node/user. Though useful in many domains, there are domains where GNNs are useful but topology is not important. This is reflected empirically for regular grid graph of the computer vision datasets where PNA does not significantly improve over other methods.