Community Detection via Facility Location

TL;DR

This paper introduces a novel community detection algorithm based on facility location theory, providing bounds on local modularity and a new measure of community quality, with scalable parallel implementation.

Contribution

It applies facility location theory to community detection, defining edge support and a new goodness measure, and offers a parallelizable algorithm with theoretical bounds.

Findings

Algorithm computes bounds on local modularity

Introduces the concept of edge support

Methods are massively parallelizable

Abstract

In this paper we apply theoretical and practical results from facility location theory to the problem of community detection in networks. The result is an algorithm that computes bounds on a minimization variant of local modularity. We also define the concept of an edge support and a new measure of the goodness of community structures with respect to this concept. We present preliminary results and note that our methods are massively parallelizable.