Multi-objective NSGA-II based community detection using dynamical evolution social network

Abstract

Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.

Community Aware Recommendation System with Explicit and Implicit Link Prediction

“…It designs a new framework to integrate the implicit relations and identify communities based on probability matrix factorization. It is designated as Integrated-Probabilistic Matrix Factorization (IN-PMF) via incorporating the IR-RKELM (Tharwat et al, 2020) and NDS-CD-DA (Tharwat et al, 2021).…”

“…The amount of resources that B receives from A is calculated as Eq. ( 9): The selected algorithm is the one presented in Tharwat et al (2021). The pseudocode of the selected algorithm is given in Algorithm 3 and the output of the algorithm is the Pareto fronts of each of the solutions of community detection of the social networks.…”

“…Furthermore, dealing with data does not have individual perspectives only, rather, it combines both individual and group perspectives. Consequently, developing an algorithm for RS that takes into account individual preference as well as his/her community and social network impact is an emerging trend (Tharwat et al, 2021). In this context, many works have exploited the existence of social networks in the prediction of users' preferences by matching users with his/her friends in terms of linking or not liking a certain item.…”