Enhancing Community Detection in Networks: A Comparative Analysis of Local Metrics and Hierarchical Algorithms
This work addresses community detection in networks, which is relevant for understanding social behavior, but it is incremental as it builds on existing methods without introducing new paradigms.
The study tackled the problem of complex community detection algorithms by evaluating local similarity metrics using the Girvan-Newman method on real networks, finding that these metrics have significant potential as indicated by modularity and NMI evaluations.
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.