Enhancing Community Detection in Networks: A Comparative Analysis of Local Metrics and Hierarchical Algorithms
Julio-Omar Palacio-Niño, Fernando Berzal
TL;DR
This work tackles the efficiency–accuracy trade-off in network community detection by evaluating local similarity metrics and local link-prediction heuristics within a hierarchical framework. It benchmarks a local-metrics-based workflow against the Girvan–Newman baseline and Radicchi’s clustering-coefficient approach, using modularity $Q$ and normalized mutual information $NMI$ to assess partition quality. Results show that several local metrics yield modularity and $NMI$ values competitive with baselines in denser networks (e.g., the Karate network, where $NMI$ approaches ~0.70 for multiple community counts) and demonstrate the potential of local properties to enable scalable detection, albeit with variable performance in sparser networks. The findings highlight the promise of local similarity properties to augment hierarchical detection and point to future work on heuristic selection and network preprocessing to further boost performance.
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.