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Accurate Path-based Methods for Influence Maximization in Social Networks

Authors:

Yun-Yong Ko,

Dong-Kyu Chae,

Sang-Wook KimAuthors Info & Claims

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

Pages 59 - 60

https://doi.org/10.1145/2872518.2889407

Published: 11 April 2016 Publication History

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Abstract

This paper proposes a novel approach to target-oriented influence estimation, which remedies the drawback of state-of-the-art, thereby understanding information diffusion more accurately in a social network.

References

[1]

W. Chen et al. Efficient influence maximization in social networks. In SIGKDD, pages 199--208. ACM, 2009.

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[2]

A. Goyal et al. Simpath: An efficient algorithm for influence maximization under the linear threshold model. In ICDM, pages 211--220. IEEE, 2011.

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[3]

D. Kempe et al. Maximizing the spread of influence through a social network. In SIGKDD, pages 137--146. ACM, 2003.

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[4]

J. Kim et al. Scalable and parallelizable processing of influence maximization for large-scale social networks? In ICDE, pages 266--277. IEEE, 2013.

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Cited By

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Ko YRyu SHan SJeon YKim JPark SHan KTong HKim S(2023)KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance PredictionProceedings of the ACM Web Conference 202310.1145/3543507.3583300(1572-1583)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583300
Min HCao JYuan TLiu B(2020)Topic based time-sensitive influence maximization in online social networksWorld Wide Web10.1007/s11280-020-00792-0Online publication date: 26-Mar-2020
https://doi.org/10.1007/s11280-020-00792-0
Wu QZhou LHuang Y(2020)Influence Maximization Based on Community Closeness in Social NetworksWeb Information Systems Engineering10.1007/978-981-15-3281-8_13(142-156)Online publication date: 6-Feb-2020
https://doi.org/10.1007/978-981-15-3281-8_13
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Index Terms

Accurate Path-based Methods for Influence Maximization in Social Networks
1. Information systems
  1. Information systems applications
    1. Data mining
2. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization

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Information & Contributors

Information

Published In

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

April 2016

1094 pages

ISBN:9781450341448

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

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.

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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Poster

Funding Sources

National Research Foundation of Korea
the Institute for Information and Communications Technology Promotion

Conference

WWW '16

Sponsor:

IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

Acceptance Rates

WWW '16 Companion Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Ko YRyu SHan SJeon YKim JPark SHan KTong HKim S(2023)KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance PredictionProceedings of the ACM Web Conference 202310.1145/3543507.3583300(1572-1583)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583300
Min HCao JYuan TLiu B(2020)Topic based time-sensitive influence maximization in online social networksWorld Wide Web10.1007/s11280-020-00792-0Online publication date: 26-Mar-2020
https://doi.org/10.1007/s11280-020-00792-0
Wu QZhou LHuang Y(2020)Influence Maximization Based on Community Closeness in Social NetworksWeb Information Systems Engineering10.1007/978-981-15-3281-8_13(142-156)Online publication date: 6-Feb-2020
https://doi.org/10.1007/978-981-15-3281-8_13
Hong SKo YJoe MKim SHung CPapadopoulos G(2019)Influence maximization for effective advertisement in social networksProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297412(1314-1321)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297412
Wu HShang JZhou SFeng YQiang BXie W(2018)LAIM: A Linear Time Iterative Approach for Efficient Influence Maximization in Large-Scale NetworksIEEE Access10.1109/ACCESS.2018.28642406(44221-44234)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2864240

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Recommendations

Influence Maximization in Online Social Networks

Continuous states latency aware influence maximization in social networks

Influence maximization algorithm based on cross propagation in location-based social networks