Advanced Influential Node Identification in Social Networks: A Hybrid Copula and Quantum-Based Deep Learning Model

Abstract Social networks are one the foremost origins of information transmission at present. Nevertheless, not all nodes in social networks are indistinguishable. As a matter of fact, certain nodes are said to be more influential than others, or to be more specific, their information gravitates to proliferate more. Identifying the most influential nodes in a social network called as the Influence Maximization problem remains one of the hot issue with the evolution of Internet and social media. Several methods have been proposed to identify influential nodes in composite networks, ranging from parallel algorithm to distribution difference and non-overlapping communities. However, most of the previous methods do not consider error involved in overlapping communities in social network. To solve the problem, we propose a method called, Copula Tubular Quantum Based Spearman Deep Neural for influential node tracing in social network (CTQ-SDNINT) is proposed. Besides, three algorithms are used in our work to find the influential node in social network with maximum influence spread, minimal time, and minimal error rate namely Copula Probability Node Pre-processing algorithm and Tubular Neighborhood Intensity-based Feature extraction algorithm Network Curb Quantum-based Spearman Deep Neural Influential Node Tracing algorithm respectively. Extensive experiments on The Telecom dataset demonstrate the efficiency and influence performance of the proposed algorithms on Performance metrics such as sensitivity, specificity, response time, accuracy, and influence spread are used to compare the proposed methods against existing ones. Results indicate that our approach significantly improves the accuracy and efficiency of influential node tracing in social networks.