Learning influence from heterogeneous social networks
Influence is a complex and subtle force that governs social dynamics and user behaviors.Understanding how users influence each other can benefit various applications, e.g., viral marketing, recommendation, information retrieval and etc.While prior work has mainly focused on qualitative aspect, in this article, we present our research in quantitatively learning influence between users in heterogeneous networks.We propose a generative graphical model which leverages both heterogeneous link information and textual content associated with each user in the network to mine topic-level influence strength.Based on the learned direct influence, we further study the influence propagation and aggregation mechanisms: conservative and non-conservative propagations to derive the indirect influence.We apply the discovered influence to user behavior prediction in four different genres of social networks: Twitter, Digg, Renren, and Citation.Qualitatively, our approach can discover some interesting influence patterns from these heterogeneous networks.Quantitatively, the learned influence strength greatly improves the accuracy of user behavior prediction.
Social influence analysis Social network analysis Influence propagation Topic modeling
Lu Liu Jie Tang Jiawei Han Shiqiang Yang
Capital Medical University, Beijing, China Tsinghua University, Beijing, China University of Illinois at Urbana-Champaign, Champaign, IL, USA
国内会议
北京
英文
402-435
2012-12-01（万方平台首次上网日期，不代表论文的发表时间）