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2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)
San Francisco, CA, USA
Aug. 18, 2016 to Aug. 21, 2016
ISBN: 978-1-5090-2847-4
pp: 1380-1381
Chenhui Zhang , Department of Computer Science and Technology, Tsinghua University, China
Sida Gao , Department of Computer Science and Technology, Tsinghua University, China
Jie Tang , Department of Computer Science and Technology, Tsinghua University, China
Tracy Xiao Liu , Department of Economics, School of Economics and Management, Tsinghua University, China
Zhanpeng Fang , Department of Computer Science and Technology, Tsinghua University, China
Xu Cheng , Tencent Corporation, Shenzhen, China
ABSTRACT
Social influence has been a widely accepted phenomenon in social networks for decades. In this paper, we study influence from the perspective of structure, and focus on the simplest group structure — triad. We analyze two different genres of behavior: Retweeting on Weibo1 and Paying on CrossFire2. We have several intriguing observations from these two networks. First, different internal structures of one's friends exhibit significant heterogeneity in influence patterns. Second, the strength of social relationship plays an important role in influencing one's behavior, and more interestingly, it is not necessarily positively correlated with the strength of social influence. We incorporate the triadic influence patterns into a predictive model to predict user's behavior. Experiment results show that our method can significantly improved the prediction accuracy.
INDEX TERMS
Social network services, Economics, Predictive models, Regression analysis, Computer science, Indexes, Logistics
CITATION

C. Zhang, S. Gao, J. Tang, T. X. Liu, Z. Fang and X. Cheng, "Learning triadic influence in large social networks," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA, 2016, pp. 1380-1381.
doi:10.1109/ASONAM.2016.7752421
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