NDLI: HYDRA: large-scale social identity linkage via heterogeneous behavior modeling

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

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

Content Provider	ACM Digital Library
Author	Liu, Siyuan Wang, Shuhui Zhang, Jinbo Krishnan, Ramayya Zhu, Feida
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 cross-platform 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.
Starting Page	51
Ending Page	62
Page Count	12
File Format	PDF
ISBN	9781450323765
DOI	10.1145/2588555.2588559
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-06-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Social networks Multiple information User linkage Heterogeneous behavior model
Content Type	Text
Resource Type	Article

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