An Advertising Game Theory Decision-Making Mechanism for Overlapping Seeds in Geo-Social Network

Social advertising (or social promotion), one of the most important application for influence maximization (IM), has become a hot industry. The purpose of social advertising is to identify a seed set of k nodes for maximizing the expected influence spread to make businesses promote products by utilizing the cascade effect. However, most existing works concerning influence maximization problem were confined to behaviors that were observed mostly in the virtual world and neglected the location information. In fact, the distance between users can also affect propagation probability during information propagation. Thus, a problem of location-aware influence maximization (LAIM) in geo-social network is formally defined in this paper. Further, a location-aware influence maximization algorithm based on greedy frame is proposed, which introduces the promotion location information into the existing IM definition and solves the problem that influence spread is not in line with actual demand in traditional IM. Due to the overlapping seeds problem caused by inevitable competition, some of the selected nodes may not work well in practice. Thus we first conduct a decision-making game from overlapping seed’s perspective to obtain Nash equilibrium by analyzing the payoff matrix of overlapping seeds and their neighbors so that the overlapping ratio of seed set and influence loss will be reduced. Finally, comprehensive experiments demonstrate the effectiveness of our algorithm and strategy.