Boosting Information Spread: An Algorithmic Approach

The majority of influence maximization (IM) studies focus on targeting influential seeders to trigger substantial information spread in social networks. Motivated by the observation that incentives could “boost” users so that they are more likely to be influenced by friends, we consider a new and complementary k-boosting problem which aims at finding k users to boost so to trigger a maximized “boosted” influence spread. The k-boosting problem is different from the IM problem because boosted users behave differently from seeders: boosted users are initially uninfluenced and we only increase their probability to be influenced. Our work also complements the IM studies because we focus on triggering larger influence spread on the basis of given seeders. Both the NP-hardness of the problem and the non-submodularity of the objective function pose challenges to the k-boosting problem. To tackle the problem on general graphs, we devise two efficient algorithms with the data-dependent approximation ratio. To tackle the problem on bidirected trees, we present
an efficient greedy algorithm and a dynamic programming that is a fully polynomial-time approximation scheme. Experiments using real social networks and synthetic bidirected trees verify the efficiency and effectiveness of the proposed algorithms. In particular, on general graphs, boosting solutions returned by our algorithms achieves boosts of influence that are up to several times higher than those achieved by boosting intuitive solutions with no approximation guarantee. We also explore the “budget allocation” problem experimentally, demonstrating the benefits of allocating the budget to both seeders and boosted users.