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Crossing the Boundaries of Communities via Limited Link Injection for Information Diffusion In Social Networks

Authors:

Dimitrios Rafailidis,

Alexandros NanopoulosAuthors Info & Claims

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

Pages 97 - 98

https://doi.org/10.1145/2740908.2742733

Published: 18 May 2015 Publication History

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Abstract

We propose a new link-injection method aiming at boosting the overall diffusion of information in social networks. Our approach is based on a diffusion-coverage score of the ability of each user to spread information over the network. Candidate links for injection are identified by a matrix factorization technique and link injection is performed by attaching links to users according to their score. We additionally perform clustering to identify communities in order to inject links that cross the boundaries of such communities. In our experiments with five real world networks, we demonstrate that our method can significantly spread the information diffusion by performing limited link injection, essential to real-world applications

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Cited By

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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
Li YYan RWu W(2019)Optimization on Content Spread in Social Network StudiesNonlinear Combinatorial Optimization10.1007/978-3-030-16194-1_13(273-284)Online publication date: 1-Jun-2019
https://doi.org/10.1007/978-3-030-16194-1_13
Lin YChen WLui J(2018)Boosting Information Spread: An Algorithmic ApproachIEEE Transactions on Computational Social Systems10.1109/TCSS.2018.28003985:2(344-357)Online publication date: Jun-2018
https://doi.org/10.1109/TCSS.2018.2800398
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Index Terms

Crossing the Boundaries of Communities via Limited Link Injection for Information Diffusion In Social Networks
1. Information systems
  1. Information systems applications

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Published In

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

May 2015

1602 pages

ISBN:9781450334730

DOI:10.1145/2740908

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

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.

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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

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Published: 18 May 2015

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WWW '15

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IW3C2

WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

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

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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
Li YYan RWu W(2019)Optimization on Content Spread in Social Network StudiesNonlinear Combinatorial Optimization10.1007/978-3-030-16194-1_13(273-284)Online publication date: 1-Jun-2019
https://doi.org/10.1007/978-3-030-16194-1_13
Lin YChen WLui J(2018)Boosting Information Spread: An Algorithmic ApproachIEEE Transactions on Computational Social Systems10.1109/TCSS.2018.28003985:2(344-357)Online publication date: Jun-2018
https://doi.org/10.1109/TCSS.2018.2800398
Mozafari NHamzeh AHashemi S(2017)Modelling information diffusion based on non-dominated friends in social networksJournal of Information Science10.1177/016555151666765643:6(801-815)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1177/0165551516667656
Lin YChen WLui J(2017)Boosting Information Spread: An Algorithmic Approach2017 IEEE 33rd International Conference on Data Engineering (ICDE)10.1109/ICDE.2017.137(883-894)Online publication date: Apr-2017
https://doi.org/10.1109/ICDE.2017.137

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Modelling information diffusion based on non-dominated friends in social networks

An enriched social behavioural information diffusion model in social networks

Hierarchical Community-Level Information Diffusion Modeling in Social Networks

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