Impact of Online Activities on Influence Maximization: A Random Walk Approach

With the popularity of OSNs, finding a set of most influential users (or nodes) so as to trigger the largest influence cascade is of significance. For example, companies may take advantage of the “wordof-mouth” effect to trigger a large cascade of purchases by offering free samples/discounts to those most influential users. This task is usually modeled as an influence maximization problem, and it has been widely studied in the past decade. However, considering that users in OSNs may participate in various kinds of online activities, e.g., giving ratings to products, joining discussion groups, etc., influence diffusion through online activities becomes even more significant. Thus, it necessitates to revisit the influence maximization problem by taking online activities into consideration. In this paper, we study the impact of online activities by formulating the influence maximization problem for social-activity networks (SANs) containing both users and online activities. To address the computation challenge, we define an influence centrality via random walks on hypergraph to measure influence, then use the Monte Carlo framework to efficiently estimate the centrality in SANs, and use the Hoeffding inequality to bound the approximation error. Furthermore, we develop a greedy-based algorithm with two novel optimization techniques to find the most influential users. By conducting extensive experiments with real-world datasets, we show that compared to the state-of-the-art algorithm IMM [20], our approach improves the accuracy of selecting the top k most influential users in SANs by up to an order of magnitude, and our approach has a comparable computation time even though it needs to handle a large amount of online activities. Furthermore, our approach is also general enough to work under different diffusion models, e.g., IC and LT models, as well as heterogeneous systems, e.g., multiple types of users and online activities in SANs.