Local resolution-limit-free Potts model for community detection

TL;DR

This paper introduces a highly accurate, scalable, and resolution-limit-free Potts model for community detection that outperforms existing algorithms in accuracy, robustness, and efficiency, applicable to very large and complex networks.

Contribution

The paper presents a novel local Potts model that avoids the resolution limit and demonstrates superior accuracy and scalability compared to existing methods.

Findings

Achieves accuracy comparable to the best algorithms

Robust to noise and scalable to large networks

Free from the resolution limit effect

Abstract

We report on an exceptionally accurate spin-glass-type Potts model for community detection. With a simple algorithm, we find that our approach is at least as accurate as the best currently available algorithms and robust to the effects of noise. It is also competitive with the best currently available algorithms in terms of speed and size of solvable systems. We find that the computational demand often exhibits superlinear scaling L^1.3 where L is the number of edges in the system, and we have applied the algorithm to synthetic systems as large as 40x10^6 nodes and over 1x10^9 edges. A previous stumbling block encountered by popular community detection methods is the so-called "resolution limit." Being a "local" measure of community structure, our Potts model is free from this resolution-limit effect, and it further remains a local measure on weighted and directed graphs. We also address the mitigation of resolution-limit effects for two other popular Potts models.