Social Network User Identity Association and Its Analysis

被引：0
|
作者：
Sun B. [1 ]
Zhang W. [1 ]
Si C.-X. [1 ]
机构：
[1] National Computer Network Emergency Response Technical Team, Coordination Center of China, Beijing
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2020年 / 43卷 / 01期
关键词：
Cross-platform; Identification; Identity association;
D O I：
10.13190/j.jbupt.2019-020
中图分类号：
学科分类号：
摘要：
The same user registers accounts on different social platforms, which makes user data scattered across multiple platforms, and these data are incomplete, unreliable and low utilization. By using these cross-platform data to discover the real identity of the same user corresponding to different accounts, cross-platform user identity association plays an important role in building detailed user profiles, recommendation systems, cross-social network link prediction and other cross-platform applications. Starting from the research status of identity association technology at home and abroad, the framework of user identity association and analysis is introduced, and the standards of identity data acquisition and social network data sets are collated. Subsequently, the technology of user identity association in recent years is analyzed and the evaluation index of identity association is summarized, and the social network data mining and analysis based on identity association is expounded. Finally, the research difficulties and hotspots of identity association are discussed and prospected. © 2020, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
引用
收藏
页码：122 / 128
页数：6
相关论文
共 47 条
  • [1] Zhou X., Liang X., Zhang H., Et al., Cross-platform identification of anonymous identical users in multiple social media networks, IEEE Transactions on Knowledge and Data Engineering, 28, 2, pp. 411-424, (2016)
  • [2] Liu S., Wang S., Zhu F., Structured learning from heterogeneous behavior for social identity linkage, IEEE Transactions on Knowledge and Data Engineering, 27, 7, pp. 2005-2019, (2015)
  • [3] Liu S., Wang S., Zhu F., Et al., Hydra: large-scale social identity linkage via heterogeneous behavior modeling, Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 51-62, (2014)
  • [4] Zafarani R., Liu H., Connecting users across social media sites: a behavioral-modeling approach, Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 41-49, (2013)
  • [5] Zhang Y., Tang J., Yang Z., Et al., Cosnet: connecting heterogeneous social networks with local and global consistency, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1485-1494, (2015)
  • [6] Korula N., Lattanzi S., An efficient reconciliation algorithm for social networks, Proceedings of the VLDB Endowment, 7, 5, pp. 377-388, (2014)
  • [7] Lu C.T., Shuai H.H., Yu P.S., Identifying your customers in social networks, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 391-400, (2014)
  • [8] Kong X., Zhang J., Yu P.S., Inferring anchor links across multiple heterogeneous social networks, Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 179-188, (2013)
  • [9] Goga O., Lei H., Parthasarathi S.H.K., Et al., Exploiting innocuous activity for correlating users across sites, Proceedings of the 22nd International Conference on World Wide Web, pp. 447-458, (2013)
  • [10] Ye N., Zhao Y.L., Bian G.Q., Et al., A schema-independent user identification algorithm in social networks, Journal of Xi'an Jiaotong University, 47, 12, pp. 19-25, (2013)
12345