Community-Hop: Enhancing Node Classification through Community Preference

In recent years, Graph Neural Networks (GNNs) have demonstrated significant influence on the analysis of graph structures by leveraging message-passing mechanisms to aggregate neighborhood information and perform various graph-related tasks from node classification to link prediction. Recently, GNNs have mostly been developed to deal with different types of graph structures, such as homophily (similar labels among connected nodes) and heterophily (dissimilar labels among connected nodes). However, existing methods lack the ability to combine node features and graph topology optimally to deal with heterophily. This paper proposes a Community-HOP-based GNN model for dealing with homophilic and heterophilic graph structures. Specifically, we incorporate valuable insights from the graph community structure to guide the feature aggregation process of the GNN layer to learn diverse graph properties and improve performance on node-level tasks. Extensive experiments on six node-level datasets under standard metrics demonstrate that the Community-HOP method surpasses existing baselines.