Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering
Mitchell Black, Lucy Lin, Weng-Keen Wong, Amir Nayyeri
Abstract
Effective resistance is a distance between the vertices of a graph that is both theoretically interesting and useful in applications. We study a variant of effective resistance called the biharmonic distance (Lipman et al., 2010) . While the effective resistance measures how well-connected two vertices are, we prove several theoretical results suggesting that the biharmonic distance measures how important an edge is to the global topology of the graph. Our theoretical results connect the biharmonic distance to well-known measures of connectivity of a graph like its total resistance and sparsity. Based on these results, we introduce two clustering algorithms using the biharmonic distance. Finally, we introduce a further generalization of the biharmonic distance that we call the k-harmonic distance. We empirically study the utility of biharmonic and k-harmonic distance for edge centrality and graph clustering.
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Cited by top-tier papers2
- Understanding Truncated Positional Encodings for Graph Neural NetworksJames Flora, Mitchell Black, Weng-Keen Wong, Amir NayyeriICML 2026
- Minimizing Total Biharmonic Distance in Large Graphs via Link RecommendationXinna Zhou, Zhongzhi ZhangKDD 2026
Builds on3
- Understanding Oversquashing in GNNs through the Lens of Effective ResistanceMitchell Black, Zhengchao Wan, Amir Nayyeri, Yusu WangICML 2023 · 116 citations
- Affinity-Aware Graph NetworksAmeya Velingker, Ali Kemal Sinop, Ira Ktena, Petar Velickovic et al.NeurIPS 2023 · 22 citations
- Rethinking the Expressive Power of GNNs via Graph BiconnectivityBohang Zhang, Shengjie Luo, Liwei Wang, Di HeICLR 2023 · 15 citations
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