Network clustering and community detection using modulus of families of loops

Heman Shakeri, Pietro Poggi-Corradini, Nathan Albin, and Caterina Scoglio
Phys. Rev. E 95, 012316 – Published 17 January 2017

Abstract

We study the structure of loops in networks using the notion of modulus of loop families. We introduce an alternate 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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  • Received 1 September 2016

DOI:https://doi.org/10.1103/PhysRevE.95.012316

©2017 American Physical Society

Physics Subject Headings (PhySH)

Networks

Authors & Affiliations

Heman Shakeri1,*, Pietro Poggi-Corradini2, Nathan Albin2, and Caterina Scoglio1

  • 1Electrical and Computer Engineering Department, Kansas State University, Manhattan, Kansas 66506, USA
  • 2Mathematics Department, Kansas State University, Manhattan, Kansas 66506, USA

  • *Corresponding author: heman@ksu.edu

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Issue

Vol. 95, Iss. 1 — January 2017

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