Modeling Multi-factor Sequential User Behavior Data over Social Networks

WANG Peng; ZHANG Peng; ZHOU Chuan; GUO Li; FANG Binxing; YANG Tao

doi:10.1049/cje.2016.03.025

Volume 25 Issue 2

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Article Navigation > Chinese Journal of Electronics > 2016 > 25(2): 364-371

WANG Peng, ZHANG Peng, ZHOU Chuan, et al., “Modeling Multi-factor Sequential User Behavior Data over Social Networks,” Chinese Journal of Electronics, vol. 25, no. 2, pp. 364-371, 2016, doi: 10.1049/cje.2016.03.025

Citation:

WANG Peng, ZHANG Peng, ZHOU Chuan, et al., “Modeling Multi-factor Sequential User Behavior Data over Social Networks,” Chinese Journal of Electronics, vol. 25, no. 2, pp. 364-371, 2016, doi: 10.1049/cje.2016.03.025

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Modeling Multi-factor Sequential User Behavior Data over Social Networks

doi: 10.1049/cje.2016.03.025

1.
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
2.
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100089, China;
3.
Beijing University of Posts and Telecommunications, Beijing 100876, China;
4.
China Information Technology Security Evaluation Center, Beijing 100085, China

Funds: This work is supported by the NSFC (No.61370025, No.61502479), 863 projects (No.2011AA01A103), National Basic Program of China (973 project) (No.2013CB329605), and the Strategic Leading Science and Technology Projects of Chinese Academy of Sciences (No.XDA06030200).

Received Date: 2014-03-12
Rev Recd Date: 2014-06-26
Publish Date: 2016-03-10

Abstract

Abstract

Modeling dynamic user behavior over online social networks not only helps us understand user behavior patterns on social networks, but also improves the performance of behavior analysis tasks. Time-varying user behavior is commonly influenced by multiple factors:user habit, social influence and external events. Existing works either consider only a part of these factors, or fail to model the dynamics behind user behavior. Thus, they cannot precisely model the user behavior. We present a generative Bayesianmodel HES to model dynamic user behavior data. We take the influential factors and user's selection process as separate latent variables, based on which we can recover the evolving patterns underneath user behavior data sequences. Empirical results on large-scale social networks show that the proposed approach outperforms existing user behavior prediction models by at least 8% w.r.t. prediction accuracy. Our work also unveils some interesting insights underneath social behavior data.
- Malicious user detection,
- User behavior,
- Social networks,
- Bayesian model,
- Social influence

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References(17)

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