Community detection is essential for analysing the structural organization schemes of complex networks that are encountered in social, biological, and communication systems. Five prominent community detection techniques, namely, the random node head technique (RNHT), the highest-degree node head technique (HDNHT), the Louvain community detection technique, the max–min technique, and the newly proposed degree–distance centroid community detection technique, were compared in this study. The DDC–CD approach uniquely combines the node degree and shortest-path distance metrics to select optimal centroids, thereby promoting the formation of balanced, well-separated community structures. Experiments were carried out on four real-world datasets—Facebook, Power-Grid, Wiki-Vote, and Gnutella—using three key evaluation metrics: the average fitness value, modularity, and runtime. The DDC–CD method consistently produced the lowest average fitness values (0.240 for Facebook, 0.340 for Power-Grid, 0.190 for Wiki-Vote, and 0.280 for Gnutella), reflecting minimal intercommunity connectivity. It also achieved higher modularity values for most datasets (0.531 for Facebook, 0.731 for Power-Grid, 0.141 for Wiki-Vote, and 0.227 for Gnutella), indicating the superior structural quality of its identified communities. While the runtime of DDC–CD was marginally higher than those of the RNHT and HDNHT, it remained computationally efficient and offered a more balanced trade-off relative to those of the max–min and Louvain techniques. In summary, the degree–distance centroid community detection technique surpassed the existing methods by generating compact, cohesive, and well-defined communities, demonstrating improved accuracy and stability.
Amedapu et al. (Wed,) studied this question.