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SAE: social analytic engine for large networks

Published: 11 August 2013 Publication History

Abstract

Online social networks become a bridge to connect our physical daily life and the virtual Web space, which not only provides rich data for mining, but also brings many new challenges. In this paper, we present a novel Social Analytic Engine (SAE) for large online social networks. The key issues we pursue in the analytic engine are concerned with the following problems: 1) at the micro-level, how do people form different types of social ties and how people influence each other? 2) at the meso-level, how do people group into communities? 3) at the macro-level, what are the hottest topics in a social network and how the topics evolve over time?
We propose methods to address the above questions. The methods are general and can be applied to various social networking data. We have deployed and validated the proposed analytic engine over multiple different networks and validated the effectiveness and efficiency of the proposed methods.

References

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[2]
L. Liu, J. Tang, J. Han, M. Jiang, and S. Yang. Mining topic-level influence in heterogeneous networks. In CIKM'10, pages 199--208, 2010.
[3]
T. Lou and J. Tang. Mining structural hole spanners through information diffusion in social networks. In WWW'13, pages 837--848, 2013.
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J. Tang, T. Lou, and J. Kleinberg. Inferring social ties across heterogeneous networks. In WSDM'12, pages 743--752, 2012.
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J. Tang, B. Wang, Y. Yang, P. Hu, Y. Zhao, X. Yan, B. Gao, M. Huang, P. Xu, W. Li, and A. K. Usadi. Patentminer: Topic-driven patent analysis and mining. In KDD'2012, pages 1366--1375, 2012.
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W. Tang, H. Zhuang, and J. Tang. Learning to infer social ties in large networks. In ECML/PKDD'11, pages 381--397, 2011.
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C. Wang, J. Han, Y. Jia, J. Tang, D. Zhang, Y. Yu, and J. Guo. Mining advisor-advisee relationships from research publication networks. In KDD'10, pages 203--212, 2010.
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Y. Yang, J. Tang, J. Keomany, Y. Zhao, Y. Ding, J. Li, and L. Wang. Mining competitive relationships by learning across heterogeneous networks. In CIKM'12, pages 1432--1441, 2012.
[12]
J. Zhang, B. Liu, J. Tang, T. Chen, and J. Li. Social influence locality for modeling retweeting behaviors. In IJCAI'13, 2013.

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  1. SAE: social analytic engine for large networks

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    cover image ACM Conferences
    KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2013
    1534 pages
    ISBN:9781450321747
    DOI:10.1145/2487575
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    Publication History

    Published: 11 August 2013

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    Author Tags

    1. social analytic engine
    2. social influence
    3. social network

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    KDD '13 Paper Acceptance Rate 125 of 726 submissions, 17%;
    Overall Acceptance Rate 605 of 4,597 submissions, 13%

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