Viral vs. Effective: Utility based influence maximization

The computational problem of Influence maximization concerns the selection of an initial set of nodes in a social network such that, by sending this set a certain message, its exposure through the network will be the highest. We propose to study this problem from a utilitarian point of view. That is, we study a model where there are two types of messages; one that is more likely to be propagated but gives a lower utility per user obtaining this message, and another that is less likely to be propagated but gives a higher utility. In our model the utility from a user that receives both messages is not necessarily the sum of the two utilities. The goal is to maximize the overall utility. Using an analysis based on bisubmodular functions, we show a greedy algorithm with a tight approximation ratio of ¹₂. We develop a dynamic programming based algorithm that is more suitable to our setting and show through extensive simulations that it outperforms the greedy algorithm.