k-Consistent Influencers in Network Data

摘要

With the prevalence of online social media such as Facebook, Twitter and YouTube, social influence analysis has attracted considerable research interests recently. Existing works on top-k influential nodes discovery find influential users at single time point only and do not capture whether the users are consistently influential over a period of time. Finding top-k consistent influencers has many interesting applications, such as targeted marketing, recommendation, experts finding, and stock market. Identifying top-k consistent influencers is a challenging task. First, we need to dynamically compute the total influence of each user at each time point from an action log. However, to find the consistent top-scorers, we need to sort and rank them at each time point. This is computationally expensive and not scalable. In this paper, we define the consistency of a node based on its influence and volatility over time. With the help of grid index, we develop an efficient algorithm called TCI to obtain the top-k consistent influencers given a time period. We conduct extensive experiments on three real world datasets to evaluate the proposed methods. We also demonstrate the usefulness of top-k consistent influencers in identifying information sources and finding experts. The experimental results demonstrate the efficiency and effectiveness of our methods.