Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (7): 124-129.doi: 10.11896/jsjkx.200600096

• Database & Big Data & Data Science • Previous Articles     Next Articles

Social Network User Influence Evaluation Algorithm Integrating Structure Centrality

TAN Qi, ZHANG Feng-li, WANG Ting, WANG Rui-jin, ZHOU Shi-jie   

  1. School of Information and Software Engineering(Software Engineering),University of Electronic Science and Technology of China,Chengdu 610054,China
  • Received:2020-06-16 Revised:2020-12-04 Online:2021-07-15 Published:2021-07-02
  • About author:TAN Qi,born in 1996,postgraduate,is a member of China Computer Federation.Her main research interests include machine learning,data mining and cascading forecasting.(tanqi1012more@163.com)
    ZHANG Feng-li,born in 1963,Ph.D,professor,doctoral supervisor,is a member of China Computer Federation.Her main research interests include network security and network engineering,cloud computing and big data,and machine learning.
  • Supported by:
    National Natural Science Foundation of China(61802033,61472064,61602096),Sichuan Regional Innovation Cooperation Project(2020YFQ0018),Science and Technology Project of Sichuan Province(2018GZ0087,2019YJ0543),Postdoctoral Fund Project(2018M643453),State Key Laboratory Project of Guangdong Province(2017B030314131) and Network and Data Security Key Laboratory of Sichuan Province(NDSMS201606).

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Abstract: In social networks,the transmission process of information can be controlled macro by tracking a small number of strongly influential users,but user influence is a kind of posterior information that cannot be predicted and can only be determined by relevant characteristics.Therefore,this paper proposes a social network user influence evaluation algorithm that integrates structural degree centrality to identify users with strong influence.As an evaluation algorithm for social network user influence,SDRank is developed based on an improved PageRank algorithm,which introduces structural degree centrality,combines the re-gulatory factor of join time and average forward number,and then calculates the user’s influence.Compared to other existing algorithms,SDRank is applicable to a broader set of scenarios from a user behavior perspective,for it doesn’t require specific information(such as personal tags,fans) that have potential forgery risks or default possibilities,and doesn’t have to exploit the under-lying information of disseminated content.This paper takes the cascade forwarding dataset of Weibo users as the experimental object,makes a visual analysis of the average forwarding number of top-K users and other relevant results,and discusses the role of user forwarding behavior in information transmission in social network.During the experiment,its accuracy,recall rate and F1-measure value are greatly improved compared with PageRank and TrustRank,and the effectiveness of SDRank algorithm is verified.

Key words: Degree centrality, Social network, User behavior, User influence

CLC Number: 

  • TP391
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