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

The paper presents three GNN architectural guidelines for combating this, which can lead to improved predictions, particularly on networks exhibiting heterophilous structure (i.e., non-homophilous labels). The design choices are motivated theoretically and intuitively and then combined into a single model that can provide better predictions on networks with heterophilous structure, as demonstrated by synthetic and real-world data experiments. The paper provides a number of interesting insights into why certain GNN architectural choices can help predictions in the case of low network homophily. Although not mentioned in their paper, a similar idea to higher-order neighborhoods (Section 3.1.2) I believe that these ideas provide further motivation for the design choices appearing in this paper and including them will strengthen some of the intuition.