HYDRA: Large-scale social identity linkage via heterogeneous behavior modeling

Siyuan Liu, Shuhui Wang, Feida Zhu, Jinbo Zhang, Ramayya Krishnan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

150 Scopus citations

Abstract

We study the problem of large-scale social identity linkage across different social media platforms, which is of critical importance to business intelligence by gaining from social data a deeper understanding and more accurate profiling of users. This paper proposes HYDRA, a solution framework which consists of three key steps: (I) modeling heterogeneous behavior by long-term behavior distribution analysis and multi-resolution temporal information matching; (II) constructing structural consistency graph to measure the high-order structure consistency on users' core social structures across different platforms; and (III) learning the mapping function by multi-objective optimization composed of both the supervised learning on pair-wise ID linkage information and the crossplatform structure consistency maximization. Extensive experiments on 10 million users across seven popular social network platforms demonstrate that HYDRA correctly identifies real user linkage across different platforms, and outperforms existing state-of-the-art algorithms by at least 20% under different settings, and 4 times better in most settings.

Original languageEnglish (US)
Title of host publicationSIGMOD 2014 - Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages51-62
Number of pages12
ISBN (Print)9781450323765
DOIs
StatePublished - 2014
Event2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014 - Snowbird, UT, United States
Duration: Jun 22 2014Jun 27 2014

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Other

Other2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014
CountryUnited States
CitySnowbird, UT
Period6/22/146/27/14

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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    Liu, S., Wang, S., Zhu, F., Zhang, J., & Krishnan, R. (2014). HYDRA: Large-scale social identity linkage via heterogeneous behavior modeling. In SIGMOD 2014 - Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (pp. 51-62). (Proceedings of the ACM SIGMOD International Conference on Management of Data). Association for Computing Machinery. https://doi.org/10.1145/2588555.2588559