Research On Influence Maximization In Social Activity Networks

With the popularity of online social networks,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 "word-of-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.In this thesis,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 cen-trality via random walks to measure influence,then use the Monte Carlo framework to efficiently estimate the centrality in SANs.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 our approach is effective and efficient when we needs to handle large amount of online activities.Moreover,it can be generalized and scaled.