A Parallel Hierarchical Approach for Community Detection on Large-scale Dynamic Networks

TL;DR

This paper introduces a parallel hierarchical algorithm for dynamic community detection in large-scale networks, efficiently updating communities with local information and hierarchical structures, showing improved performance and scalability.

Contribution

A novel parallel hierarchical Leiden-based algorithm that incrementally updates communities in dynamic networks using local neighborhoods and hierarchical structures.

Findings

Demonstrates improved performance and scalability on various networks.

Maintains high modularity in dynamic community detection.

Efficiently updates communities with local information.

Abstract

In this paper, we propose a novel parallel hierarchical Leiden-based algorithm for dynamic community detection. The algorithm, for a given batch update of edge insertions and deletions, partitions the network into communities using only a local neighborhood of the affected nodes. It also uses the inner hierarchical graph-based structure, which is updated incrementally in the process of optimizing the modularity of the partitioning. The algorithm has been extensively tested on various networks. The results demonstrate promising improvements in performance and scalability while maintaining the modularity of the partitioning.