Affiliations: [a] Department of Computer Engineering, Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, Nerul, Navi Mumbai, Maharashtra, India | [b] Department of Information Technology, Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, Nerul, Navi Mumbai, Maharashtra, India
Correspondence:
[*]
Corresponding authors: Vishakha Shelke, Department of Computer Engineering, Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, Nerul, Navi Mumbai, 400706, Maharashtra, India. E-mail: vishakhashelke21@gmail.com. Ashish Jadhav, Department of Information Technology, Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, Nerul, Navi Mumbai, 400706, Maharashtra, India. E-mail: ashish.jadhav@rait.ac.in.
Abstract: Influence maximization (IM) in dynamic social networks is an optimization problem to analyze the changes in social networks for different periods. However, the existing IM methods ignore the context propagation of interaction behaviors among users. Hence, context-based IM in multiplex networks is proposed here. Initially, multiplex networks along with their contextual data are taken as input. Community detection is performed for the network using the Wilcoxon Hypothesized K-Means (WH-KMA) algorithm. From the detected communities, the homogeneous network is used for extracting network topological features, and the heterogeneous networks are used for influence path analysis based on which the node connections are weighted. Then, the influence-path-based features along with contextual features are extracted. These extracted features are given for the link prediction model using the Parametric Probability Theory-based Long Short-Term Memory (PPT-LSTM) model. Finally, from the network graph, the most influencing nodes are identified using the Linear Scaling based Clique (LS-Clique) detection algorithm. The experimental outcomes reveal that the proposed model achieves an enhanced performance.
Keywords: IM, social influence analysis, multiplex networks, Wilcoxon Hypothesized community detection, linear scaling based influencing nodes identification, parametric probability theory-based link prediction
