A model-independent approach for efficient influence maximization in social networks

The well-known influence maximization problem (Kempe et al., in proceedings of the 9th SIGKDD international conference on knowledge discovery and data mining (KDD), pp 137–146, 2003 ) (or viral marketing through social networks) deals with selecting a few influential initial seeds to maximize the awareness of product(s) over the social network. As it is computationally hard (Kempe et al., in proceedings of the 9th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 137–146, 2003 ), a greedy approximation algorithm is designed to address the influence maximization problem. However, the major drawback of this greedy algorithm is that it runs extremely slow even on network datasets consisting of a few thousand nodes and edges (Leskovec et al., in proceedings of the 13th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 420–429, 2007 ; Checn et al., in proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 937–944, 2009 ). Several efficient heuristics have been proposed in the literature (Checn et al., in proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 937–944, 2009 ) to alleviate this computational difficulty; however, these heuristics are designed for specific influence propagation models such as linear threshold model and independent cascade model. This motivates the strong need to design an approach that not only works with any influence propagation model, but also efficiently solves the influence maximization problem. In this paper, we precisely address this problem by proposing a new framework which fuses both link and interaction data to come up with a backbone for a given social network, which can further be used for efficient influence maximization. We then conduct thorough experimentation with several real-life social network datasets such as DBLP, Epinions, Digg, and Slashdot and show that the proposed approach is efficient as well as scalable.