A smart local moving algorithm for large-scale modularity-based community detection

TL;DR

This paper presents a novel smart local moving algorithm for large-scale community detection that outperforms existing methods like Louvain in modularity optimization and is computationally efficient for massive networks.

Contribution

It introduces a more sophisticated local moving heuristic for community detection, achieving higher modularity and efficiency in large networks.

Findings

Outperforms Louvain algorithm in modularity scores

Efficiently handles networks with tens of millions of nodes

Achieves high modularity in small and medium networks

Abstract

We introduce a new algorithm for modularity-based community detection in large networks. The algorithm, which we refer to as a smart local moving algorithm, takes advantage of a well-known local moving heuristic that is also used by other algorithms. Compared with these other algorithms, our proposed algorithm uses the local moving heuristic in a more sophisticated way. Based on an analysis of a diverse set of networks, we show that our smart local moving algorithm identifies community structures with higher modularity values than other algorithms for large-scale modularity optimization, among which the popular 'Louvain algorithm' introduced by Blondel et al. (2008). The computational efficiency of our algorithm makes it possible to perform community detection in networks with tens of millions of nodes and hundreds of millions of edges. Our smart local moving algorithm also performs well in small and medium-sized networks. In short computing times, it identifies community structures with modularity values equally high as, or almost as high as, the highest values reported in the literature, and sometimes even higher than the highest values found in the literature.