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

2025-02-20

AI Summary

To address the low efficiency and poor real-time adaptability of community detection in large-scale dynamic networks, this paper proposes a parallel hierarchical Leiden incremental algorithm. Our method introduces a novel “local-update-driven” optimization mechanism that reconstructs only the affected node neighborhoods and their nested hierarchical graph structures upon edge insertions or deletions, integrating modularity optimization, incremental graph maintenance, and parallelized local pruning. Experiments on diverse real-world dynamic networks demonstrate that, compared to static Leiden, our approach achieves speedups of 8.3–15.6× while preserving or even improving modularity (average gain +1.2%) and reducing memory overhead by 37%–52%. These results significantly enhance real-time responsiveness and scalability, establishing an efficient and robust new paradigm for community detection in ultra-large-scale dynamic graphs.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Detects communities in dynamic networks
• Uses parallel hierarchical Leiden algorithm
• Optimizes modularity with local updates

Innovation

• Parallel hierarchical Leiden-based algorithm
• Local neighborhood partitioning
• Incremental modularity optimization