Edge based stochastic block model statistical inference

TL;DR

This paper introduces an edge sampling-based stochastic block model (SBM) for community detection, providing a rigorous statistical inference method to identify meaningful communities in graphs, tested on synthetic and real networks.

Contribution

It presents a novel edge sampling-based SBM definition and a corresponding quality function for statistical inference of community structure.

Findings

Effective community detection on synthetic graphs

Successful application to Zachary karate club network

Provides a rigorous statistical framework for SBM inference

Abstract

Community detection in graphs often relies on ad hoc algorithms with no clear specification about the node partition they define as the best, which leads to uninterpretable communities. Stochastic block models (SBM) offer a framework to rigorously define communities, and to detect them using statistical inference method to distinguish structure from random fluctuations. In this paper, we introduce an alternative definition of SBM based on edge sampling. We derive from this definition a quality function to statistically infer the node partition used to generate a given graph. We then test it on synthetic graphs, and on the zachary karate club network.