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research-article

Recommendations to boost content spread in social networks

Authors:

Rajeev Rastogi,

Rushi BhattAuthors Info & Claims

WWW '12: Proceedings of the 21st international conference on World Wide Web

Pages 529 - 538

https://doi.org/10.1145/2187836.2187908

Published: 16 April 2012 Publication History

Abstract

Content sharing in social networks is a powerful mechanism for discovering content on the Internet. The degree to which content is disseminated within the network depends on the connectivity relationships among network nodes. Existing schemes for recommending connections in social networks are based on the number of common neighbors, similarity of user profiles, etc. However, such similarity-based connections do not consider the amount of content discovered.

In this paper, we propose novel algorithms for recommending connections that boost content propagation in a social network without compromising on the relevance of the recommendations. Unlike existing work on influence propagation, in our environment, we are looking for edges instead of nodes, with a bound on the number of incident edges per node. We show that the content spread function is not submodular, and develop approximation algorithms for computing a near-optimal set of edges. Through experiments on real-world social graphs such as Flickr and Twitter, we show that our approximation algorithms achieve content spreads that are as much as 90 times higher compared to existing heuristics for recommending connections.

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Cited By

Wang JWu YWang XZhang YQin LZhang WLin X(2024)Efficient Influence Minimization via Node BlockingProceedings of the VLDB Endowment10.14778/3675034.367504217:10(2501-2513)Online publication date: 6-Aug-2024
https://doi.org/10.14778/3675034.3675042
Chen XSong YTang JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Link Recommendation to Augment Influence Diffusion with Provable GuaranteesProceedings of the ACM Web Conference 202410.1145/3589334.3645521(2509-2518)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645521
Eckles DMossel ERahimian MSen S(2024)Long ties accelerate noisy threshold-based contagionsNature Human Behaviour10.1038/s41562-024-01865-08:6(1057-1064)Online publication date: 22-Apr-2024
https://doi.org/10.1038/s41562-024-01865-0
Show More Cited By

Index Terms

Recommendations to boost content spread in social networks
1. Information systems
  1. Information storage systems
    1. Storage architectures
      1. Storage network architectures

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '12: Proceedings of the 21st international conference on World Wide Web

April 2012

1078 pages

ISBN:9781450312295

DOI:10.1145/2187836

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Univ. de Lyon: Universite de Lyon

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

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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WWW 2012

Sponsor:

Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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

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Cited By

Wang JWu YWang XZhang YQin LZhang WLin X(2024)Efficient Influence Minimization via Node BlockingProceedings of the VLDB Endowment10.14778/3675034.367504217:10(2501-2513)Online publication date: 6-Aug-2024
https://doi.org/10.14778/3675034.3675042
Chen XSong YTang JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Link Recommendation to Augment Influence Diffusion with Provable GuaranteesProceedings of the ACM Web Conference 202410.1145/3589334.3645521(2509-2518)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645521
Eckles DMossel ERahimian MSen S(2024)Long ties accelerate noisy threshold-based contagionsNature Human Behaviour10.1038/s41562-024-01865-08:6(1057-1064)Online publication date: 22-Apr-2024
https://doi.org/10.1038/s41562-024-01865-0
Wang ZSong JZhao J(2023)Product Promotion in Social Networks with Crowd EffectSSRN Electronic Journal10.2139/ssrn.4365346Online publication date: 2023
https://doi.org/10.2139/ssrn.4365346
Li WCai MGao MWen DQin LWang W(2023)Expanding Reverse Nearest NeighborsProceedings of the VLDB Endowment10.14778/3636218.363622017:4(630-642)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.14778/3636218.3636220
Wang YYang ZLiu JZhang WCui B(2023)Scapin: Scalable Graph Structure Perturbation by Augmented Influence MaximizationProceedings of the ACM on Management of Data10.1145/35892911:2(1-21)Online publication date: 20-Jun-2023
https://doi.org/10.1145/3589291
Chakraborty PRanu SMantri KDe AChua TLauw HSi LTerzi ETsaparas P(2023)Learning and Maximizing Influence in Social Networks Under Capacity ConstraintsProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570433(733-741)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570433
Yao XGao NGu CHuang H(2023)Enhance Rumor Controlling Algorithms Based on Boosting and Blocking Users in Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.318233710:5(2698-2712)Online publication date: Oct-2023
https://doi.org/10.1109/TCSS.2022.3182337
Golzardi ESheikhahmadi AAbdollahpouri A(2023)TRTCD: trust route prediction based on trusted community detectionMultimedia Tools and Applications10.1007/s11042-023-15096-482:27(41571-41607)Online publication date: 4-Apr-2023
https://doi.org/10.1007/s11042-023-15096-4
Huang SLin WBao ZSun J(2022)Influence maximization in real-world closed social networksProceedings of the VLDB Endowment10.14778/3565816.356582116:2(180-192)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.14778/3565816.3565821
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