An Efficient Influence Maximization Algorithm Based on Social Relationship Priority in Mobile Social Networks

Xinxin Zhang, Li Xu, Min Gao

Abstract: The mobile social network (MSN) combines techniques in social science and wireless communications for mobile networking. The MSN can be considered as a system which provides a variety of data delivery services involving the social relationship among mobile users. The key problem in MSNs is Influence Maximization (IM), which aims at finding the top-k influential users from the mobile social network and contributing to the spread of maximum information, the users may have different attitudes (positive/negative) towards a message when the message appears in MSNs. In this paper, we first model the mobile social network as the topological graph based on social priority topological to study the social influence. Then we innovatively propose a scheme which integrates ITÖ algorithm into PSO algorithm to solve the problem of maximizing the influence in MSNs. Finally, experimental evaluation shows that the scheme we proposed to identify influential nodes is more accurate and efficient than other schemes by comparison, and the probability of maximizing the influence of our scheme can reach to \(56\%\).