A Heuristic Approach to Identify Influential Node in Complex Social Networks

Multiple features of the complex network are exhibited among the nodes in time-varying social networks. Community detection has numerous approaches, but the majority of methods are restricted to disjoint community situations. Solutions to disjoint community situations do not reflect well on real-world social networks. An innovative heuristic method has been proposed to recognize community structure in real-world networks and to identify the most influential node within each community. The proposed concept on a recognized community network has been tested in order to validate it. This work introduced one new network metric, the Dependency factor which computes the influence of a node. Our results are compared with other existing approaches using real-life datasets. We have compared the dependency factor with other network centralities. The computational case study is done over a well-known Bengali novel, namely Chaar Adhyay written by Nobel laureate Rabindranath Tagore. The analytical results are more accurate than the state-of-the-art approaches.