Maximizing the Cumulative Influence through a Social Network when Repeat Activation Exists

We study the problem of employing social networks for propagate influence when repeated activations are involved. While influence maximization has been extensively studied as the fundamental solution, it neglects the reality that a user may purchase a product/service repeatedly, incurring cumulative sales of the product/service. In this paper, we explore a new problem of cumulative influence maximization that brings the influence maximization a step closer to real-world viral marketing applications. In our problem setting, repeated activation exists and we aim to find a set of initial users, through which the maximal cumulative influence can be stimulated in a given time period. To describe the repeated activation behavior, we adopt the voter model to reflect the variation of activations over time. Under the voter model, we formulate the maximization problem and present an effective algorithm. We test and compare our method with heuristic algorithms on real-world data sets. Experimental results demonstrate the utility of the proposed method