Enhancing Community Detection in Networks: A Comparative Analysis of Local Metrics and Hierarchical Algorithms

TL;DR

This paper compares local similarity metrics and hierarchical algorithms for community detection in networks, evaluating their effectiveness using modularity and NMI on real-world networks.

Contribution

It introduces an evaluation of local similarity metrics within the Girvan-Newman framework for community detection, highlighting their potential.

Findings

Local metrics show significant potential for community detection.

Evaluation on real networks demonstrates effectiveness of local metrics.

Hierarchical algorithms can be enhanced using local similarity measures.

Abstract

The analysis and detection of communities in network structures are becoming increasingly relevant for understanding social behavior. One of the principal challenges in this field is the complexity of existing algorithms. The Girvan-Newman algorithm, which uses the betweenness metric as a measure of node similarity, is one of the most representative algorithms in this area. This study employs the same method to evaluate the relevance of using local similarity metrics for community detection. A series of local metrics were tested on a set of networks constructed using the Girvan-Newman basic algorithm. The efficacy of these metrics was evaluated by applying the base algorithm to several real networks with varying community sizes, using modularity and NMI. The results indicate that approaches based on local similarity metrics have significant potential for community detection.