Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering

Understand

Graph neural networks (GNNs) based methods have achieved impressive performance on node clustering task.

• However, they are designed on the homophilic assumption of graph and clustering on heterophilic graph is overlooked.
• Due to the lack of labels, it is impossible to first identify a graph as homophilic or heterophilic before a suitable GNN model can be found.
• Hence, clustering on real-world graph with various levels of homophily poses a new challenge to the graph research community.