Real-time topic-aware influence maximization using preprocessing

BACKGROUND: Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks are typically mixtures of topics. METHODS: In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods to avoid redoing influence maximization for each mixture from scratch. RESULTS: We explore two preprocessing algorithms with theoretical justifications. CONCLUSIONS: Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40649-016-0033-z) contains supplementary material, which is available to authorized users.