A Study on Influential User Identification in Online Social Networks

WANG Nan; SUN Qindong; ZHOU Yadong; SHEN Si

doi:10.1049/cje.2016.05.012

Volume 25 Issue 3

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WANG Nan, SUN Qindong, ZHOU Yadong, et al., “A Study on Influential User Identification in Online Social Networks,” Chinese Journal of Electronics, vol. 25, no. 3, pp. 467-473, 2016, doi: 10.1049/cje.2016.05.012

Citation:

WANG Nan, SUN Qindong, ZHOU Yadong, et al., “A Study on Influential User Identification in Online Social Networks,” Chinese Journal of Electronics, vol. 25, no. 3, pp. 467-473, 2016, doi: 10.1049/cje.2016.05.012

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A Study on Influential User Identification in Online Social Networks

doi: 10.1049/cje.2016.05.012

1.
Shaanxi Key Laboratory of Network Computing and Security, Xi'an University of Technology, Xi'an 710048, China;
2.
Ministry of Education Key Laboratory For Intelligent Network and Network Security, Xi'an Jiaotong University, Xi'an 710049, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61172124, No.61571360, No.61202392).

More Information

Corresponding author: SUN Qindong was born in 1975. He is now the professor in the Department Computer Science and Engineering of Xi'an University of Technology. His research interests include network information security, online social networks and web of things. (Email: sqd@xaut.edu.cn)
Received Date: 2015-07-06
Rev Recd Date: 2015-12-07
Publish Date: 2016-05-10

Abstract

Abstract

Influential user evaluation is great importance in many application areas of online social networks. In order to identify influential users in a more adequate and practical way, we propose a Dynamic regional interaction model (DRI) to evaluate user influence in online social networks. Influential users can be identified by the influence effect on different distance users based on dynamic regional interaction model. We have applied the influential user identification method to Sina Weibo and the experimental results show that compared with the existing methods the proposed method can identify the influence users in a more accuracy and efficiency way.
- Online social networks,
- Influential user identification,
- Dynamic regional interaction model (DRI),
- User influence evaluation

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