Identifying the hub nodes (the most influential users) in an information diffusion network is a central research topic in social influence analysis. In [2], we developed the Multi-task Sparse Linear Influence Model (MSLIM) to automatically select topic-sensitive influential nodes in an implicit network. Our method firstly needs to determine the active node set to model the global volume of the contagions of the entire network nodes. The active node set is chosen by a simple rule in [2]. However, different set leads to different model performance. Reference [4] proposed a new framework called Slow Intelligence System, which can continuously learn, search the appropriate method and propagate information according to the environment to improve the performance over time. In this paper, we develop a slow intelligence based active user definition system by utilizing user profile visualization to help interactively determine active node sets in MSLIM model. We apply our system on a set of 2.6 million tweets of 1000 users on twitter. We show that our system can efficiently utilize user's profile visualization techniques to facilitate determining the appropriate active node sets.
Models and algorithms for social influence analysis
