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arXiv · 1609.00461

Network clustering and community detection using modulus of families of loops

Heman Shakeri·Pietro Poggi-Corradini·Nathan Albin·Caterina Scoglio
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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.

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Heman Shakeri, Pietro Poggi-Corradini, Nathan Albin, Caterina Scoglio. 2016-09-02. Network clustering and community detection using modulus of families of loops. https://doi.org/10.1103/physreve.95.012316

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