Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering

However, they are designed on of connected nodes belong to different classes (Pei et al., the homophilic assumption of graph and clustering 2020; Xie et al., 2023). Traditional GNNs learn representations on heterophilic graph is overlooked. Due to via message passing mechanism under the assumption the lack of labels, it is impossible to first identify of homophily (Fang et al., 2022). Facing heterophilic graphs, a graph as homophilic or heterophilic before a previous approaches mainly suffer two limitations. On the suitable GNN model can be found. Hence, clustering one hand, the local neighbors in a graph are nodes that are on real-world graph with various levels of proximally located, while nodes that are semantically similar homophily poses a new challenge to the graph might be far apart on heterophilic graph (Zhu et al., research community. To fill this gap, we propose 2020). Thus, existing techniques fail to capture long-range a novel graph clustering method, which contains information from distant nodes. On the other hand, they three key components: graph reconstruction, don't distinguish similar and dissimilar neighbors, which a mixed filter, and dual graph clustering carry different amounts of information.