Multistep greedy algorithm identifies community structure in real-world and computer-generated networks

TL;DR

This paper introduces a multistep greedy algorithm extension for community detection that improves modularity optimization and produces more accurate community partitions in real-world and synthetic networks.

Contribution

It presents an empirical formula for selecting step width l, enhancing community detection by avoiding local optima in modularity optimization.

Findings

The multistep greedy algorithm outperforms the original in real-world networks.

It produces higher modularity scores and more reasonable community partitions.

Empirical results show improved detection in both real and synthetic networks.

Abstract

We have recently introduced a multistep extension of the greedy algorithm for modularity optimization. The extension is based on the idea that merging l pairs of communities (l>1) at each iteration prevents premature condensation into few large communities. Here, an empirical formula is presented for the choice of the step width l that generates partitions with (close to) optimal modularity for 17 real-world and 1100 computer-generated networks. Furthermore, an in-depth analysis of the communities of two real-world networks (the metabolic network of the bacterium E. coli and the graph of coappearing words in the titles of papers coauthored by Martin Karplus) provides evidence that the partition obtained by the multistep greedy algorithm is superior to the one generated by the original greedy algorithm not only with respect to modularity but also according to objective criteria. In other words, the multistep extension of the greedy algorithm reduces the danger of getting trapped in local optima of modularity and generates more reasonable partitions.