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LINKREC: a unified framework for link recommendation with user attributes and graph structure

Published: 26 April 2010 Publication History

Abstract

With the phenomenal success of networking sites (e.g., Facebook, Twitter and LinkedIn), social networks have drawn substantial attention. On online social networking sites, link recommendation is a critical task that not only helps improve user experience but also plays an essential role in network growth. In this paper we propose several link recommendation criteria, based on both user attributes and graph structure. To discover the candidates that satisfy these criteria, link relevance is estimated using a random walk algorithm on an augmented social graph with both attribute and structure information. The global and local influence of the attributes is leveraged in the framework as well. Besides link recommendation, our framework can also rank attributes in a social network. Experiments on DBLP and IMDB data sets demonstrate that our method outperforms state-of-the-art methods based on network structure and node attribute information for link recommendation.

References

[1]
L. Getoor and C. P. Diehl. Link mining: a survey. SIGKDD Explorations, 7(2):3--12, 2005.
[2]
D. Liben-Nowell and J. M. Kleinberg. The link prediction problem for social networks. In CIKM, pp. 556--559, 2003.
[3]
H. Tong, C. Faloutsos, and J.-Y. Pan. Fast random walk with restart and its applications. In ICDM, pp. 613--622, 2006.

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  • (2024)Link prediction using extended neighborhood based local random walk in multilayer social networksJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10193136:2(101931)Online publication date: Feb-2024
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  • (2023)A link prediction method based on compressed sensing for social networksApplied Intelligence10.1007/s10489-023-05060-y53:23(29300-29318)Online publication date: 1-Dec-2023
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  1. LINKREC: a unified framework for link recommendation with user attributes and graph structure

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    Published In

    cover image ACM Other conferences
    WWW '10: Proceedings of the 19th international conference on World wide web
    April 2010
    1407 pages
    ISBN:9781605587998
    DOI:10.1145/1772690

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 26 April 2010

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    Author Tags

    1. link recommendation
    2. random walk

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    WWW '10
    WWW '10: The 19th International World Wide Web Conference
    April 26 - 30, 2010
    North Carolina, Raleigh, USA

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

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    • (2024)Link prediction using extended neighborhood based local random walk in multilayer social networksJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10193136:2(101931)Online publication date: Feb-2024
    • (2023)Recommending on graphs: a comprehensive review from a data perspectiveUser Modeling and User-Adapted Interaction10.1007/s11257-023-09359-w33:4(803-888)Online publication date: 13-Mar-2023
    • (2023)A link prediction method based on compressed sensing for social networksApplied Intelligence10.1007/s10489-023-05060-y53:23(29300-29318)Online publication date: 1-Dec-2023
    • (2022)Encoding edge type information in graphletsPLOS ONE10.1371/journal.pone.027360917:8(e0273609)Online publication date: 26-Aug-2022
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    • (2020)Edge2vecACM Transactions on Knowledge Discovery from Data10.1145/339129814:4(1-24)Online publication date: 30-May-2020
    • (2020)Disclose More and Risk Less: Privacy Preserving Online Social Network Data SharingIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2018.286140317:6(1173-1187)Online publication date: 1-Nov-2020
    • (2020)Link Prediction on Social Attribute Network Using Lévy Flight Firefly OptimizationAdvances in Artificial Intelligence and Data Engineering10.1007/978-981-15-3514-7_97(1299-1309)Online publication date: 14-Aug-2020
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