Network clustering and community detection using modulus of families of loops

Abstract

We study the structure of loops in networks using the notion of modulus of loop families. We introduce a new measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among loops by spreading the expected link-usage optimally. We propose weighting networks using these expected link-usages to improve classical community detection algorithms. We show that the proposed method enhances the performance of certain algorithms, such as spectral partitioning and modularity maximization heuristics, on standard benchmarks.

arXiv comment8 pages, 20 figures

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Cited by in corpus (4)

• Blocking duality for $p$-modulus on networks and applications
• A new method for quantifying network cyclic structure to improve community detection
• Generalization of Effective Conductance Centrality for Egonetworks
• Modulus of time-respecting paths