Comparing community structure identification

TL;DR

This paper compares recent community detection methods focusing on their accuracy and computational efficiency, highlighting the trade-offs and proposing a benchmark for evaluating such algorithms.

Contribution

It introduces a standard benchmark for community detection methods and analyzes the trade-offs between accuracy and computational cost.

Findings

More accurate methods are generally more computationally expensive.

The modularity measure is revisited and evaluated.

A benchmark test for community detection methods is proposed.

Abstract

We compare recent approaches to community structure identification in terms of sensitivity and computational cost. The recently proposed modularity measure is revisited and the performance of the methods as applied to ad hoc networks with known community structure, is compared. We find that the most accurate methods tend to be more computationally expensive, and that both aspects need to be considered when choosing a method for practical purposes. The work is intended as an introduction as well as a proposal for a standard benchmark test of community detection methods.