Team formation with influence maximization for influential event organization on social networks

Abstract

Online event-based social services allow users to organize social events by specifying the themes, and invite friends to participate social events. While the event information can be spread over the social network, it is expected that by certain communication between event hosts, users interested in the event themes can be as more as possible. In this paper, by combining the ideas of team formation and influence maximization, we formulate a novel research problem, Influential Team Formation (ITF), to facilitate the organization of social events. Given a set L of required labels to describe the event topics, a social network, and the size k of the host team, ITF is to find a k-node set S that satisfying L and maximizing the Influence-Cost Ratio (i.e., the influence spread per communication cost between team members). Since ITF is proved to be NP-hard, we develop two greedy algorithms and one heuristic method to solve it. Extensive experiments conducted on Facebook and Google+ datasets exhibit the effectiveness and efficiency of the proposed methods. In addition, by employing the real event participation data in Meetup, we show that ITF with the proposed solutions is able to predict organizers of influential events.