Review for NeurIPS paper: Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs

The authors argue that we need to enable graph neural nets to model graphs beyond homophily, which is reasonable and great. However, the three corresponding designs that are introduced to address this issue lack of technical novelty and depth. All of the three designs have been proposed and well utilized (in a separated way) in existing graph neural nets. The proposed H2GNN model puts all three design together without clear discussions about their original sources during the authors' arguments (though table 2 is used in related work). Furthermore, the goal of the three designs is to model heterophily in graphs or networks.