Conference Proceeding Article
For better viral marketing, there has been a lot of research on social influence maximization. However, the problem that who is influenced and how diverse the influenced population is, which is important in real-world marketing, has largely been neglected. To that end, in this paper, we propose to consider the magnitude of influence and the diversity of the influenced crowd simultaneously. Specifically, we formulate it as an optimization problem, i.e., diversified social influence maximization. First, we present a general framework for this problem, under which we construct a class of diversity measures to quantify the diversity of the influenced crowd. Meanwhile, we prove that a simple greedy algorithm guarantees to provide a near-optimal solution to the optimization problem. Furthermore, we relax the problem by focusing on the diversity of the nodes targeted for initial activation, and show how this relaxed form could be used to diversify the results of many heuristics, e.g., PageRank. Finally, we run extensive experiments on two real-world datasets, showing that our formulation is effective in generating diverse results.
diversity, influence maximization, viral marketing
Databases and Information Systems | Marketing | Public Relations and Advertising
Data Management and Analytics
ASONAM 2014: Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining: 17-20 August 2014, Beijing
City or Country
Tang, Fangshuang; Liu, Qi; Zhu, Hengshu; Chen, Enhong; and ZHU, Feida.
Diversified Social Influence Maximization. (2014). ASONAM 2014: Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining: 17-20 August 2014, Beijing. 455-459. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2653