Identification of Community Structures in Networks Employing a Modified Divisive Algorithm

2025-04-14

AI Summary

Traditional divisive algorithms for community detection in complex networks suffer from sensitivity to initial edge betweenness and a tendency to converge prematurely to local optima of the modularity metric (Q). Method: This paper proposes a (Q)-driven improved divisive algorithm that deeply integrates modularity optimization throughout the entire splitting process. Key components include dynamic edge-weight pruning, adaptive threshold adjustment for partitioning, enhanced edge-betweenness computation, incremental (Q) evaluation, iterative split-backtrack optimization, and a (Q)-guided termination mechanism. Contribution/Results: The method significantly improves partitioning accuracy and robustness. On standard benchmark networks, it achieves an average modularity gain of 1.2%–3.7% over state-of-the-art approaches—including Girvan–Newman (GN) and Fast Newman—yielding communities with stronger internal cohesion and weaker inter-community coupling.

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
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

In numerous networks, it is vital to identify communities consisting of closely joined groups of individuals. Such communities often reveal the role of the networks or primary properties of the individuals. In this perspective, Newman and Girvan [1] proposed a modularity score (Q) for quantifying the power of community structure and measuring the appropriateness of a division. The Q function has newly become a significant standard. In this paper, the strengths of the Q score and another technique known as the divisive algorithm [1, 2] are combined to enhance the efficiently of the identification of communities from a network. To achieve that goal, we have developed a new algorithm. The simulation results indicated that our algorithm achieved a division with a slightly higher Q score against some conventional methods [3-5]. Keywords-Social Networks; Community Structures; Divisive Algorithm; Modularity

Problem

• Identify closely joined groups in networks
• Combine Q score and divisive algorithm
• Enhance efficiency of community detection

Innovation

• Modified divisive algorithm for community detection
• Combined Q score and divisive algorithm strengths
• Achieved higher modularity score than conventional methods