JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2019, Vol. 54 ›› Issue (9): 105-113.doi: 10.6040/j.issn.1671-9352.1.2018.115

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Social network user identity linkage model based on comprehensive information

YANG Ya-ru*, WANG Yong-qing, ZHANG Zhi-bin, LIU Yue, CHENG Xue-qi   

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  • Online:2019-09-20 Published:2019-07-30

Abstract: With the increasing popularity of social media sites, users prefer to join multiple online social networks, how to associate multiple user identities in a social network as a new job in social media is of great significance. This paper studies a representative user identity linkage model and proposes a user identity linkage model based on comprehensive information(BiALP). The model learns the internal structure information, attribute information and content information of the network through the node expression method. It is characterized by the node representation of the source network and the target network, and is based on the associated user pairs. With tagged data, the relationship between the source network and the target network is learned through a binary supervised learning manner. A large number of experiments have shown that the BiALP model has a significant improvement(35%)compared to other representative user identity linkage models.

Key words: user linkage, network embedding, supervised learning

CLC Number: 

  • TP391
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