Mod-CSA: Modularity optimization by conformational space annealing

TL;DR

Mod-CSA is a novel stochastic optimization method that enhances modularity detection in large networks, outperforming traditional techniques in efficiency and accuracy by leveraging conformational space annealing.

Contribution

This paper introduces Mod-CSA, a new modularity optimization algorithm based on conformational space annealing, improving results and scalability over existing methods.

Findings

Achieves higher modularity partitions than previous methods.

Requires less computational resources for large graphs.

Can be combined with other heuristics and parallelized.

Abstract

We propose a new modularity optimization method, Mod-CSA, based on stochastic global optimization algorithm, conformational space annealing (CSA). Our method outperforms simulated annealing in terms of both efficiency and accuracy, finding higher modularity partitions with less computational resources required. The high modularity values found by our method are higher than, or equal to, the largest values previously reported. In addition, the method can be combined with other heuristic methods, and implemented in parallel fashion, allowing it to be applicable to large graphs with more than 10000 nodes.