A data-based approach to social influence maximization

Influence maximization is the problem of finding a set of users in a social network, such that by targeting this set, one maximizes the expected spread of influence in the net-work. Most of the literature on this topic has focused ex-clusively on the social graph, overlooking historical data, i.e., traces of past action propagations. In this paper, we study influence maximization from a novel data-based per-spective. In particular, we introduce a new model, which we call credit distribution, that directly leverages available propagation traces to learn how influence flows in the net-work and uses this to estimate expected influence spread. Our approach also learns the different levels of influence-ability of users, and it is time-aware in the sense that it takes the temporal nature of influence into account. We show that influence maximization under the credit dis-tribution model is NP-hard and that the function that de-fines expected spread under our model is submodular. Based on these, we develop an approximation algorithm for solving the influence maximization problem that at once enjoys high accuracy compared to the standard approach, while being several orders of magnitude faster and more scalable. 1