Network clustering and community detection using modulus of families of   loops
  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. 
Heman Shakeri, Pietro Poggi-Corradini, Nathan Albin, and Caterina   Scoglio
cs.SI physics.soc-ph