finding influential individual in Social Network graphs using CSCS algorithm and shapley value in game theory

In recent years, the social networks analysis gains great deal of attention. Social networks have various applications in different areas namely predicting disease epidemic, search engines and viral advertisements. A key property of social networks is that interpersonal relationships can influence the decisions that they make. Finding the most influential nodes is important in social networks because such nodes can greatly affect many other people. In this paper, diffusion among nodes was investigated using the centrality of Shapely value, dividing a network into communities in the linear threshold and the independent cascade model. Furthermore, this algorithm was evaluated by different datasets and compared with benchmark algorithms.