Scalable Methods for Adaptively Seeding a Social Network

Thibaut Horel, Yaron Singer

Abstract: In many applications of influence maximization, one is restricted to select influencers from a set of users who engaged with the topic being promoted, and due to the structure of social networks, these users often rank low in terms of their influence potential. To alleviate this issue, one can consider an adaptive method which selects users in a manner which targets their influential neighbors. The advantage of such an approach is that it leverages the friendship paradox in social networks: while users are often not influential, they often know someone who is. Despite the various complexities in such optimization problems, we show that scalable adaptive seeding is achievable. To show the effectiveness of our methods we collected data from various verticals social network users follow, and applied our methods on it. Our experiments show that adaptive seeding is scalable, and that it obtains dramatic improvements over standard approaches of information dissemination.