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User Modeling in Large Social Networks

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Yuxiao DongAuthors Info & Claims

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Page 713

https://doi.org/10.1145/2835776.2855087

Published: 08 February 2016 Publication History

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Abstract

This proposal aims to harness the power of data, social, and network sciences to model user behavior in social networks. Specifically, we focus on individual users and investigate the interplay between their behavior and subsequently emergent social phenomena. Work in this proposal unveils the significant social strategies that are used by people to satisfy their social needs. We apply computational methods to address user modeling problems, including demographic inference, link recommendation, and social impact prediction. The proposed research work can be translated into applications in large social systems, such as mobile communication, online social media, and academic collaboration.

References

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Y. Dong, R. A. Johnson, and N. V. Chawla. Will this paper increase your phh-index? scientific impact prediction. In WSDM '15, pages 149--158, 2015.

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[2]

Y. Dong, R. A. Johnson, Y. Yang, and N. V. Chawla. Collaboration signatures reveal scientific impact. In ASONAM '15, pages 480--487, 2015.

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[3]

Y. Dong, J. Tang, T. Lou, B. Wu, and N. V. Chawla. How long will she call me? distribution, social theory and duration prediction. In Machine Learning and Knowledge Discovery in Databases, pages 16--31. Springer, 2013.

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[4]

Y. Dong, J. Tang, S. Wu, J. Tian, N. V. Chawla, J. Rao, and H. Cao. Link prediction and recommendation across heterogeneous social networks. In ICDM'12, pages 181--190, 2012.

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Y. Dong, J. Zhang, J. Tang, N. V. Chawla, and B. Wang. Coupledlp: Link prediction in coupled networks. In KDD '15, pages 199--208, 2015.

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Cited By

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Rahman MSadat SAsyhari ARefat NKabir MArshah R(2021)A Secure and Sustainable Framework to Mitigate Hazardous Activities in Online Social NetworksIEEE Transactions on Sustainable Computing10.1109/TSUSC.2019.29111886:1(30-42)Online publication date: 1-Jan-2021
https://doi.org/10.1109/TSUSC.2019.2911188
Mezhuyev VSadat SRahman MRefat NAsyhari A(2019)Evaluation of the Likelihood of Friend Request Acceptance in Online Social NetworksIEEE Access10.1109/ACCESS.2019.29212197(75318-75329)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2921219

Index Terms

User Modeling in Large Social Networks
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology
2. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

February 2016

746 pages

ISBN:9781450337168

DOI:10.1145/2835776

General Chairs:
Paul N. Bennett
Microsoft Research
,
Vanja Josifovski
Pinterest
,
Program Chairs:
Jennifer Neville
Purdue University
,
Filip Radlinski
Microsoft

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 February 2016

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Abstract

Funding Sources

U.S. Air Force Office of Scientific Research (AFOSR) and the Defense Advanced Research Projects Agency (DARPA)
Army Research Laboratory
National Science Foundation

Conference

WSDM 2016

Sponsor:

WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining

February 22 - 25, 2016

California, San Francisco, USA

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WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

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Rahman MSadat SAsyhari ARefat NKabir MArshah R(2021)A Secure and Sustainable Framework to Mitigate Hazardous Activities in Online Social NetworksIEEE Transactions on Sustainable Computing10.1109/TSUSC.2019.29111886:1(30-42)Online publication date: 1-Jan-2021
https://doi.org/10.1109/TSUSC.2019.2911188
Mezhuyev VSadat SRahman MRefat NAsyhari A(2019)Evaluation of the Likelihood of Friend Request Acceptance in Online Social NetworksIEEE Access10.1109/ACCESS.2019.29212197(75318-75329)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2921219

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Understanding user behavior in a local social media platform by social network analysis

Characterizing user behavior in online social networks

Characterizing user navigation and interactions in online social networks