HeNCler: Node Clustering in Heterophilous Graphs via Learned Asymmetric Similarity

Sonny Achten, Francesco Tonin, Volkan Cevher, Johan Suykens

Keywords: Heterophily, Node Clustering, Singular Value Decomposition

TL;DR: We propose an approach for node clustering through a learned asymmetric similarity graph showing performance gains in heterophilous graphs.

Abstract:

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 **He**terophilous **N**ode **Cl**ust**er**ing.
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. By solving the primal problem directly, our method overcomes the computational difficulties of traditional adjacency partitioning-based approaches. Experimental results show that HeNCler significantly improves node clustering performance in heterophilous graph settings, highlighting the advantage of its asymmetric graph-learning framework.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 5007