HeNCler: Node Clustering in Heterophilous Graphs via Learned Asymmetric Similarity

Understand

Clustering nodes in heterophilous graphs is challenging as traditional methods assume that effective clustering is characterized by high intra-cluster and low inter-cluster connectivity.

• To address this, we introduce HeNCler-a novel approach for Heterophilous Node Clustering.
• HeNCler learns a similarity graph by optimizing a clustering-specific objective based on weighted kernel singular value decomposition.
• Our approach enables spectral clustering on an asymmetric similarity graph, providing flexibility for both directed and undirected graphs.