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On Crawling Community-aware Online Social Network Data

Published: 12 September 2019 Publication History

Abstract

In this paper, we make our first attempt to address this issue by proposing a new research problem to identify a group of users from the OSN, such that the group is a socially tight community and the users' willingness of data contribution is maximized. We propose an effective algorithm, named Community-aware Group Identification with Maximum Willingness (CIW). We conduct extensive experiments on multiple real datasets, and the proposed CIW outperforms the other baselines in terms of both solution quality and efficiency.

References

[1]
B. Balasundaram, S. Butenko, and I. V. Hicks. Clique relaxations in social network analysis: The maximum k-plex problem. Operations Research, 59(1):133--142, 2011.
[2]
W. Chen, Y. Wang, and S. Yang. Efficient influence maximization in social networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 199--208. ACM, 2009.
[3]
J. Cheng, Y. Ke, A. W.-C. Fu, J. X. Yu, and L. Zhu. Finding maximal cliques in massive networks by h*-graph. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pages 447--458. ACM, 2010.
[4]
Y. Fang, R. Cheng, S. Luo, and J. Hu. Effective community search for large attributed graphs. Proceedings of the VLDB Endowment, 9(12):1233--1244, 2016.
[5]
M. Gjoka, M. Kurant, C. T. Butts, and A. Markopoulou. Practical recommendations on crawling online social networks. IEEE Journal on Selected Areas in Communications, 29(9):1872--1892, 2011.
[6]
X. Huang, L. V. Lakshmanan, J. X. Yu, and H. Cheng. Approximate closest community search in networks. Proceedings of the VLDB Endowment, 9(4):276--287, 2015.
[7]
R. Laishram, J. D. Wendt, and S. Soundarajan. Crawling the community structure of multiplex networks. In AAAI, 2019.
[8]
J. Leskovec and J. J. Mcauley. Learning to discover social circles in ego networks. In Advances in neural information processing systems, pages 539--547, 2012.
[9]
R.-H. Li, L. Qin, J. X. Yu, and R. Mao. Influential community search in large networks. Proceedings of the VLDB Endowment, 8(5):509--520, 2015.
[10]
F. Zhang, Y. Zhang, L. Qin, W. Zhang, and X. Lin. When engagement meets similarity: efficient (k, r)-core computation on social networks. Proceedings of the VLDB Endowment, 10(10):998--1009, 2017.
[11]
Y. Zhang and S. Parthasarathy. Extracting analyzing and visualizing triangle k-core motifs within networks. In 2012 IEEE 28th International Conference on Data Engineering (ICDE), pages 1049--1060. IEEE, 2012.endthebibliography

Cited By

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  • (2024)Diversity-Optimized Group Extraction in Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.322493511:1(756-769)Online publication date: Feb-2024
  • (2022)Real-Time Focused Extraction of Social Media UsersIEEE Access10.1109/ACCESS.2022.316897710(42607-42622)Online publication date: 2022
  • (2020)WMEgo: Willingness Maximization for Ego Network Data Extraction in Online Social NetworksProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411867(515-524)Online publication date: 19-Oct-2020
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Published In

cover image ACM Conferences
HT '19: Proceedings of the 30th ACM Conference on Hypertext and Social Media
September 2019
326 pages
ISBN:9781450368858
DOI:10.1145/3342220
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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New York, NY, United States

Publication History

Published: 12 September 2019

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Author Tags

  1. crawling
  2. social networks
  3. willingness

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HT '19 Paper Acceptance Rate 20 of 68 submissions, 29%;
Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

View all
  • (2024)Diversity-Optimized Group Extraction in Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.322493511:1(756-769)Online publication date: Feb-2024
  • (2022)Real-Time Focused Extraction of Social Media UsersIEEE Access10.1109/ACCESS.2022.316897710(42607-42622)Online publication date: 2022
  • (2020)WMEgo: Willingness Maximization for Ego Network Data Extraction in Online Social NetworksProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411867(515-524)Online publication date: 19-Oct-2020
  • (2020)Detecting Social Anxiety with Online Social Network Data2020 21st IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM48529.2020.00073(333-336)Online publication date: Jun-2020
  • (2020)CrawlSN: community-aware data acquisition with maximum willingness in online social networksData Mining and Knowledge Discovery10.1007/s10618-020-00709-5Online publication date: 8-Sep-2020

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