Context-based influence maximization with privacy protection in social networks

As the increase of requirements of accessing and sharing information of people, large social networks have appeared. The influence maximization over social network has been a popular research topic, whose goal is to maximize the expected range of influence by selecting seed nodes to sending information and encouraging nodes in social network to report the messages. On the other side, privacy concerns have became more and more important, both automated and manual efforts are utilized to protection privacy of users. Under the mechanisms of privacy protection, the nodes in social network will not act as they did in the setting of no privacy protection. As far as we know, there are no previous works considering this problem. In this paper, we consider the influence maximization problem with privacy protection mechanisms in social networks. One challenge is that how to abstract the relations between users and information to identify which kinds of information should be protected by the privacy-related mechanisms. A context-based solution is proposed in the paper to face the challenge above and solve the influence maximization problem. First, a context-based information diffusion model (IDC for short) is proposed. Then, the corresponding influence maximization problem (IM-IDC for short) under IDC model is formally defined. Then, the methods about context extraction, influence estimation, and redundant contexts identification are introduced. The IM-IDC problem is shown to be NP-hard, and an efficient approximation algorithm based on greedy strategy is proposed and analyzed theoretically. Finally, experimental results show that our method is efficient.