: A social network – the graph of relationships and interactions within a group of individuals – plays a fundamental role as a medium for spread of information, ideas, and influence among its members. One of the major problems of social networks is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence, which refers to influence maximization. Game theory is a strong tool for analyzing and modeling the situations in which one’s benefit depends on his own choice and others’ behavior. In fact, there is an attempt to form a mathematical and logical formulation from the players’ activities in strategic conditions in which players play with each other to get the best results. Evolutionary stable strategy (ESS) is a known topic in game theory which has a similar modality as an influence maximization problem that can be used to analyze this problem. In this project, we developed a framework based on evolutionary game theory to analyze the influence maximization problem. In this framework, we calculate the distinction factor of each user based on their characteristics and split those with rather the same factor to three clusters. An evolutionary strategy game will be formed by these three clusters as its players and their influence on other clusters as their payoffs. Then, we study the evolution of these clusters, so we can decide how much of each should be selected as seed nodes to maximize influence propagation and check if it’s stable. Key Words: social networks analysis, influence maximization, game theory, evolutionary stable strategy
