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Link recommendation for promoting information diffusion in social networks

Published: 13 May 2013 Publication History

Abstract

Online social networks mainly have two functions: social interaction and information diffusion. Most of current link recommendation researches only focus on strengthening the social interaction function, but ignore the problem of how to enhance the information diffusion function. For solving this problem, this paper introduces the concept of user diffusion degree and proposes the algorithm for calculating it, then combines it with traditional recommendation methods for reranking recommended links. Experimental results on Email dataset and Amazon dataset under Independent Cascade Model and Linear Threshold Model show that our method noticeably outperforms the traditional methods in terms of promoting information diffusion.

References

[1]
Chaoji,V., Ranu,S., Rastogi, R., and Bhatt, R. Recommendations to boost content spread in social networks. In WWW, pages 529--538, 2012.
[2]
Chen, W., Wang, C., and Wang, Y. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In KDD, pp. 1029--1038, 2010.
[3]
Girvan, M. and Newman, M. E. J. Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821--7826(2002)
[4]
Liben-Nowell, D., and Kleinberg, J. The link prediction problem for social networks. In CIKM, pp. 556--559, 2003.
[5]
Yin, D., Hong, L., and Davison. B. D. Structural link analysis and prediction in microblogs. In CIKM, pp 1163--1168, 2011.

Cited By

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  • (2023)Maximizing the Diversity of Exposure in Online Social Networks by Identifying Users with Increased Susceptibility to PersuasionACM Transactions on Knowledge Discovery from Data10.1145/362582618:2(1-21)Online publication date: 14-Nov-2023
  • (2021)Efficient computation of target-oriented link criticalness centrality in uncertain graphsIntelligent Data Analysis10.3233/IDA-20553925:5(1323-1343)Online publication date: 1-Jan-2021
  • (2019)Marginal Gains to Maximize Content Spread in Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2019.29118656:3(479-490)Online publication date: Jun-2019
  • Show More Cited By

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  1. Link recommendation for promoting information diffusion in social networks

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    Published In

    cover image ACM Other conferences
    WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web
    May 2013
    1636 pages
    ISBN:9781450320382
    DOI:10.1145/2487788
    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.

    Sponsors

    • NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
    • CGIBR: Comite Gestor da Internet no Brazil

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 13 May 2013

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    Author Tags

    1. diffusion degree
    2. information diffusion
    3. link recommendation

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    • Poster

    Conference

    WWW '13
    Sponsor:
    • NICBR
    • CGIBR
    WWW '13: 22nd International World Wide Web Conference
    May 13 - 17, 2013
    Rio de Janeiro, Brazil

    Acceptance Rates

    WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;
    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    Cited By

    View all
    • (2023)Maximizing the Diversity of Exposure in Online Social Networks by Identifying Users with Increased Susceptibility to PersuasionACM Transactions on Knowledge Discovery from Data10.1145/362582618:2(1-21)Online publication date: 14-Nov-2023
    • (2021)Efficient computation of target-oriented link criticalness centrality in uncertain graphsIntelligent Data Analysis10.3233/IDA-20553925:5(1323-1343)Online publication date: 1-Jan-2021
    • (2019)Marginal Gains to Maximize Content Spread in Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2019.29118656:3(479-490)Online publication date: Jun-2019
    • (2017)Maximizing Network Performance Based on Group Centrality by Creating Most Effective k-Links2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA.2017.44(561-570)Online publication date: Oct-2017
    • (2017)Finding k most influential edges on flow graphsInformation Systems10.1016/j.is.2016.12.00265:C(93-105)Online publication date: 1-Apr-2017
    • (2017)A user behavior influence model of social hotspot under implicit linkInformation Sciences: an International Journal10.1016/j.ins.2017.02.035396:C(114-126)Online publication date: 1-Aug-2017
    • (2016)Centrality-Aware Link RecommendationsProceedings of the Ninth ACM International Conference on Web Search and Data Mining10.1145/2835776.2835818(503-512)Online publication date: 8-Feb-2016
    • (2015)A dynamic influence model of social network hotspot based on grey system一种基于灰色系统理论的热点话题用户行为影响力模型Science China Information Sciences10.1007/s11432-015-5439-y58:12(1-12)Online publication date: 16-Oct-2015

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