Optimized Ranking-Based Community Detection

Complex network analysis and community detection have gained lots of traction among researchers due to their transdisciplinary applications from target marketing to networked brain modeling. Ranking the detected communities can be of interest in applications where the critical and influential nodes play a major role in future directions of the community. An open research problem is the analysis of correlations among ranked members and their relation to popular nodes. One approach is to rank the nodes based on information flow in data networks. In this study, we evaluate the feasibility of ranker-oriented community detection using an optimized approach based on node strength.