Effectiveness of Internal Evaluation Metrics for Community Detection Based on Clustering

The exploration of complex networks and the arrangement of communities is a widely researched topic across various fields, reflecting research interest in a multitude of domains. Clustering algorithms have emerged as a prominent tool for community detection, gaining considerable attention in recent decades. To assess the effectiveness of clustering algorithms, various evaluation metrics are employed, including internal, external, and relative metrics. In this paper, the effectiveness of several partitional clustering algorithms is analyzed to identify communities. The algorithms reviewed include graph-based, centroid-based, and modal-based algorithms, which were tested on various datasets. The study’s primary aim is to determine how accurate and reliable internal evaluation metrics are for community detection through clustering. The study’s findings reveal that the k-means algorithm excelled in silhouette score and sum of squared error evaluation, while affinity propagation outperformed others in terms of the davies-bouldin index and adjusted mutual information. These results can provide valuable guidance and support in the domain of community detection, aiding researchers in achieving more accurate and effective analyses of complex network structures.