More Web Proxy on the site http://driver.im/

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.

References

[1]

Official Facebook Blog:. http://blog.facebook.com/blog.php?post=15610312130.

[2]

Discovering who to follow. Official Twitter Blog. http://blog.twitter.com/2010/07/discovering-who-to-follow.html.

[3]

J. Chen, W. Geyer, C. Dugan, M. Muller, and I. Guy. Make new friends, but keep the old: recommending people on social networking sites. In CHI '09, pages 201--210, Boston, MA, USA, 2009.

Digital Library

[4]

W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In KDD '10, pages 1029--1038, Washington, DC, USA, 2010.

Digital Library

[5]

W. Chen, Y. Wang, and S. Yang. Efficient influence maximization in social networks. In KDD '09, pages 199--208, New York, NY, USA, 2009.

Digital Library

[6]

E. D. Demaine and M. Zadimoghaddam. Minimizing the diameter of a network using shortcut edges. In SWAT, pages 420--431, 2010.

Digital Library

[7]

P. Domingos and M. Richardson. Mining the network value of customers. In KDD '01, pages 57--66, San Francisco, California, 2001.

Digital Library

[8]

A. Ghosh and S. Boyd. Growing well-connected graphs. In Growing Well-connected Graphs, pages 6605 -- 6611, 2006.

[9]

J. Goldenberg, B. Libai, and E. Muller. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, pages 211--223, August 2001.

[10]

M. Granovetter. Threshold models of collective behavior. American Journal of Sociology, (83):1420--1443, 1978.

[11]

I. Guy, I. Ronen, and E. Wilcox. Do you know?: recommending people to invite into your social network. In IUI '09, pages 77--86, 2009.

Digital Library

[12]

J. Hannon, M. Bennett, and B. Smyth. Recommending twitter users to follow using content and collaborative filtering approaches. In RecSys '10, pages 199--206, 2010.

Digital Library

[13]

B. Huang and T. Jebara. Loopy belief propagation for bipartite maximum weight b-matching. In M. Meila and X. Shen, editors, AISTATS '07, volume 2 of JMLR: W&CP, March 2007.

[14]

D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In KDD '03, pages 137--146, Washington, D.C., 2003.

Digital Library

[15]

D. Kempe, J. M. Kleinberg, and É. Tardos. Influential nodes in a diffusion model for social networks. In ICALP'05, pages 1127--1138, 2005.

Digital Library

[16]

J. Leskovec, D. Huttenlocher, and J. Kleinberg. Predicting positive and negative links in online social networks. In WWW '10, pages 641--650, 2010.

Digital Library

[17]

J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance. Cost-effective outbreak detection in networks. In KDD '07, pages 420--429, San Jose, California, USA, 2007.

Digital Library

[18]

D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In CIKM '03, pages 556--559, New Orleans, LA, USA, 2003.

Digital Library

[19]

M. E. J. Newman. The structure and function of complex networks. SIAM Review, 45:167--256, 2003.

Digital Library

[20]

M. Roth, A. Ben-David, D. Deutscher, G. Flysher, I. Horn, A. Leichtberg, N. Leiser, Y. Matias, and R. Merom. Suggesting friends using the implicit social graph. In KDD '10, pages 233--242, Washington, DC, USA, 2010.

Digital Library

[21]

R. Schifanella, A. Barrat, C. Cattuto, B. Markines, and F. Menczer. Folks in folksonomies: social link prediction from shared metadata. In WSDM '10, pages 271--280, New York, NY, USA, 2010.

Digital Library

[22]

B. Suh, L. Hong, P. Pirolli, and E. H. Chi. Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In SOCIALCOM '10, pages 177--184, Washington, DC, USA, 2010. IEEE Computer Society.

Digital Library

[23]

J. Vondrák. Optimal approximation for the submodular welfare problem in the value oracle model. In STOC, pages 67--74, 2008.

Digital Library

[24]

R. Zafarani and H. Liu. Social computing data repository at ASU, 2009.

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

Recommendations

Context aware recommendations for user-generated content on a social network site
EuroITV '09: Proceedings of the 7th European Conference on Interactive TV and Video

The enormous offer of video content on the internet and its continuous growth make the selection process increasingly difficult for end-users. This overabundance of audio-visual material can be handled by a recommendation system that observes user ...
Friend recommendation with content spread enhancement in social networks

Social network is becoming an increasingly popular media for information sharing. More and more people are interacting with others via major social network sites such as Twitter and Flickr. An important aspect of a social network is its capability in ...
Exploring acquaintances of social network site users for effective social event recommendations

In this paper, we propose a social event recommendation method that exploits a user's social interaction relations and collaborative friendships to recommend events of interest. A challenge of the social event recommendation is that social events, such ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

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]

Sponsors

Univ. de Lyon: Universite de Lyon

In-Cooperation

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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WWW 2012

Sponsor:

Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

84
Total Citations
View Citations
986
Total Downloads

Downloads (Last 12 months)34
Downloads (Last 6 weeks)1

Reflects downloads up to 23 Dec 2024

Other Metrics

View Author Metrics

Citations

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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents