A Hybrid Probabilistic Graph and Link Prediction Model for Complex Social Networking Data - B. Mohammed Ismail , L. V. Narasimha Prasad, Rajasekhar Nennuri, S. Iwin Thanakumar Joseph (International Journal of Intelligent Systems and Applications in Engineering, 2024)
1 Research scholar, Department of computer science and engineering, Koneru Lakshmaiah education foundation, Vaddeshwaram, AP, India 2Assistant Professor, Koneru Lakshmaiah Education Foundation, Vaddeshwaram, AP, India 3P.A. College of Engineering Mangalore, Affiliated to Visvesvaraya Technological University Belgum. 4Professor, Institute of Aeronautical Engineering, Hyderabad. In this complex datasets of social networking, the possibility based graph community identification acts a prominent role. As many of the traditional models are intricate in estimating the novel link prediction type by utilizing benchmark graph community grouping measures. Besides conventional clustering measures utilize measures of nearest-neighbour regardless of contextual identicality for estimating the association amongst diversified nodes of graph. For optimizing contextual clustering of node & estimation of link, the hybrid scalable measure has been projected for clustering the community on intricate networks. Hence, in this research, the hybrid clustering graph & link prediction models have been projected on intricate social-networking dataset for effective patterns of decision-making. The simulation outcomes assist that projected contextual probabilistic-graph-clustering & link estimation model is having better effectiveness when compared to traditional approaches on intricate datasets of social-networking.