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Influence maximization is the problem of finding a set of users in a socialnetwork, such that by targeting this set, one maximizes the expected spread ofinfluence in the network. Most of the literature on this topic has focusedexclusively on the social graph, overlooking historical data, i.e., traces ofpast action propagations. In this paper, we study influence maximization from anovel data-based perspective. In particular, we introduce a new model, which wecall credit distribution, that directly leverages available propagation tracesto learn how influence flows in the network and uses this to estimate expectedinfluence spread. Our approach also learns the different levels ofinfluenceability of users, and it is time-aware in the sense that it takes thetemporal nature of influence into account. We show that influence maximizationunder the credit distribution model is NP-hard and that the function thatdefines expected spread under our model is submodular. Based on these, wedevelop an approximation algorithm for solving the influence maximizationproblem that at once enjoys high accuracy compared to the standard approach,while being several orders of magnitude faster and more scalable.

A Data-Based Approach to Social Influence Maximization