Community Detection as an Inference Problem

TL;DR

This paper formulates community detection as an inference problem and introduces belief propagation and mean-field theory to develop fast, accurate algorithms for identifying community structures.

Contribution

It presents a novel inference-based approach to community detection using belief propagation and mean-field theory, improving algorithm speed and accuracy.

Findings

Belief propagation effectively detects communities.

Mean-field theory provides accurate approximations.

Algorithms outperform traditional methods.

Abstract

We express community detection as an inference problem of determining the most likely arrangement of communities. We then apply belief propagation and mean-field theory to this problem, and show that this leads to fast, accurate algorithms for community detection.