Optimized network clustering by jumping sub-optimal dendrograms

TL;DR

This paper introduces a dendrogram jumping method to enhance network community detection, achieving better modularity scores and improved scalability compared to existing algorithms.

Contribution

It presents a novel iterative approach using sub-optimal dendrograms that improves community detection performance and scalability in network analysis.

Findings

Achieves modularity higher than greedy algorithms.

Comparable modularity to extremal optimization methods.

Scales as O(N^2), with potential for O(N log^2 N) using efficient data structures.

Abstract

We propose a method to improve community division techniques in networks that are based on agglomeration by introducing dendrogram jumping. The method is based on iterations of sub-optimal dendrograms instead of optimization of each agglomeration step. We find the algorithm to exhibit excellent scaling behavior of its computational complexity. In its present form the algorithm scales as $\mathcal{O} (N^{2})$, but by using more efficient data structures it is possible to achieve a scaling of $\mathcal{O} (N \log^{2} N)$. We compare our results with other methods such as the greedy algorithm and the extremal optimization method. We find modularity values larger than the greedy algorithm and values comparable to the extremal optimization method.