poster

Link recommendation for promoting information diffusion in social networks

Authors:

Dong Li,

Zhiming Xu,

Sheng Li,

Xin Sun,

Anika Gupta,

Katia SycaraAuthors Info & Claims

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

Pages 185 - 186

https://doi.org/10.1145/2487788.2487881

Published: 13 May 2013 Publication History

Get Access

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.

Digital Library

Google Scholar

[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.

Digital Library

Google Scholar

[3]

Girvan, M. and Newman, M. E. J. Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821--7826(2002)

Crossref

Google Scholar

[4]

Liben-Nowell, D., and Kleinberg, J. The link prediction problem for social networks. In CIKM, pp. 556--559, 2003.

Digital Library

Google Scholar

[5]

Yin, D., Hong, L., and Davison. B. D. Structural link analysis and prediction in microblogs. In CIKM, pp 1163--1168, 2011.

Digital Library

Google Scholar

Cited By

View all

Zareie ASakellariou R(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
https://dl.acm.org/doi/10.1145/3625826
Saito KFushimi TOhara KKimura MMotoda H(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
https://dl.acm.org/doi/10.3233/IDA-205539
Yang WMa JLi YYan RYuan JWu WLi D(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
https://doi.org/10.1109/TCSS.2019.2911865
Show More Cited By

Index Terms

Link recommendation for promoting information diffusion in social networks
1. Information systems
  1. Information storage systems
    1. Storage architectures
      1. Storage network architectures

Recommendations

Link Recommendation for Social Influence Maximization
Social link recommendation systems, like “People-you-may-know” on Facebook, “Who-to-follow” on Twitter, and “Suggested-Accounts” on Instagram assist the users of a social network in establishing new connections with other users. While these systems are ...
Diversity Preference-Aware Link Recommendation for Online Social Networks
Link recommendation, such as “People You May Know” on LinkedIn, recommends links to connect unlinked online social network users. Existing link recommendation methods tend to recommend similar friends to a user but overlook the fact that different users ...
Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications. Existing link recommendation methods tend to recommend similar friends to a ...
A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions

Link recommendation has attracted significant attention from both industry practitioners and academic researchers. In industry, link recommendation has become a standard and most important feature in online social networks, prominent examples of which ...

Comments

Information & Contributors

Information

Published In

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

May 2013

1636 pages

ISBN:9781450320382

DOI:10.1145/2487788

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

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

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

Check for updates

Author Tags

Qualifiers

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%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
348
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)1

Reflects downloads up to 03 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zareie ASakellariou R(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
https://dl.acm.org/doi/10.1145/3625826
Saito KFushimi TOhara KKimura MMotoda H(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
https://dl.acm.org/doi/10.3233/IDA-205539
Yang WMa JLi YYan RYuan JWu WLi D(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
https://doi.org/10.1109/TCSS.2019.2911865
Ohara KSaito KKimura MMotoda H(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
https://doi.org/10.1109/DSAA.2017.44
Wong PSun CLo EYiu MWu XZhao ZHubert Chan TKao B(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
https://dl.acm.org/doi/10.1016/j.is.2016.12.002
Xiao YLi NXu MLiu Y(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
https://dl.acm.org/doi/10.1016/j.ins.2017.02.035
Parotsidis NPitoura ETsaparas PBennett PJosifovski VNeville JRadlinski F(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
https://dl.acm.org/doi/10.1145/2835776.2835818
Xiao YMa JLiu YYan Z(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
https://doi.org/10.1007/s11432-015-5439-y

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.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Link Recommendation for Social Influence Maximization

Diversity Preference-Aware Link Recommendation for Online Social Networks

A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions

Comments

Information

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations