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

arXiv: https://arxiv.org/abs/2408.09072 · HTML: https://mlsanity.com/paper/2408.09072/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 36 references that are in the map.
- First posted on arXiv 17 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [Link Prediction with Node Clustering Coefficient](https://mlsanity.com/paper/1510.07819/) (2015)

## Abstract (arXiv)

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.