title: 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 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.
authors.affiliation: Electrical and Computer Engineering Department, Kansas State University, Manhattan, Kansas 66506, USA.
authors.forename: Heman
authors.initials: H
authors.lastname: Shakeri
authors.affiliation: Mathematics Department, Kansas State University, Manhattan, Kansas 66506, USA.
authors.forename: Pietro
authors.initials: P
authors.lastname: Poggi-Corradini
authors.affiliation: Mathematics Department, Kansas State University, Manhattan, Kansas 66506, USA.
authors.forename: Nathan
authors.initials: N
authors.lastname: Albin
authors.affiliation: Electrical and Computer Engineering Department, Kansas State University, Manhattan, Kansas 66506, USA.
authors.forename: Caterina
authors.initials: C
authors.lastname: Scoglio