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Using link semantics to recommend collaborations in academic social networks

Published: 13 May 2013 Publication History

Abstract

Social network analysis (SNA) has been explored in many contexts with different goals. Here, we use concepts from SNA for recommending collaborations in academic networks. Recent work shows that research groups with well connected academic networks tend to be more prolific. Hence, recommending collaborations is useful for increasing a group's connections, then boosting the group research as a collateral advantage. In this work, we propose two new metrics for recommending new collaborations or intensification of existing ones. Each metric considers a social principle (homophily and proximity) that is relevant within the academic context. The focus is to verify how these metrics influence in the resulting recommendations. We also propose new metrics for evaluating the recommendations based on social concepts (novelty, diversity and coverage) that have never been used for such a goal. Our experimental evaluation shows that considering our new metrics improves the quality of the recommendations when compared to the state-of-the-art.

References

[1]
L. M. Aiello et al. Friendship prediction and homophily in social media. ACM TWeb, 6(2), 2012.
[2]
R. A. Baeza-Yates and B. A. Ribeiro-Neto. Modern Information Retrieval - the concepts and technology behind search. Pearson Education Ltd., 2011.
[3]
A.-L. Barabasi. Linked: The New Science of Networks. Perseus Books Group, 2002.
[4]
M. A. Brandão and M. M. Moro. Affiliation Influence on Recommendation in Academic Social Networks. In Procs. of AMW, 2012.
[5]
X. Cai et al. Learning collaborative filtering and its application to people to people recommendation in social networks. In Procs. of ICDM, 2010.
[6]
J. Cleland-Huang et al. The Detection and Classification of Non-Functional Requirements with Application to Early Aspects. In Procs. of RE, 2006.
[7]
N. S. Contractor, S. Wasserman, and K. Faust. Testing Multitheoretical, Multilevel Hypotheses about Organizational Networks: An Analytic Framework and Empirical Example. Acad. Manag. Review, 31(3), 2006.
[8]
F. Fouss and M. Saerens. Evaluating Performance of Recommender Systems: An Experimental Comparison. In Procs. of WI-IAT, 2008.
[9]
J. Freyne et al. Social networking feeds: recommending items of interest. In Procs. of RecSys, 2010.
[10]
I. Guy, I. Ronen, and E. Wilcox. Do you know?: recommending people to invite into your social network. In Procs. of IUI, 2009.
[11]
J. He and W. W. Chu. A Social Network-Based Recommender System (SNRS). Technical Report 090014, UCLA, 2010.
[12]
J. L. Herlocker et al. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22(1), 2004.
[13]
Z. Huang. Link Prediction Based on Graph Topology: The Predictive Value of Generalized Clustering. In Procs. of LinkKDD, 2006.
[14]
A. H. F. Laender et al. CiênciaBrasil - The Brazilian Portal of Science and. In Procs. of SEMISH, 2011.
[15]
G. R. Lopes et. Collaboration Recommendation on Academic Social Networks. In ER Workshops, 2010.
[16]
G. R. Lopes et al. Ranking Strategy for Graduate Programs Evaluation. In Procs. of ICITA, 2011.
[17]
T. Menzies et al. Problems with Precision: A Response to ''Comments on 'Data Mining Static Code Attributes to Learn Defect Predictors'''. IEEE Trans. SE, 33(9), 2007.
[18]
M. E. J. Newman. The Structure and Function of Complex Networks. SIAM Review, 45(2), 2003.
[19]
D. Quercia and L. Capra. FriendSensing: recommending friends using mobile phones. In Procs. of RecSys, 2009.
[20]
G. Shani and A. Gunawardana. Evaluating Recommendation Systems. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook. Springer, 2010.
[21]
P. Symeonidis, E. Tiakas, and Y. Manolopoulos. Transitive node similarity for link prediction in social networks with positive and negative links. In Procs. of RecSys, 2010.
[22]
D. Wang et al. Human mobility, social ties, and link prediction. In Procs. of KDD, 2011.
[23]
C.-N. Ziegler et al. Improving recommendation lists through topic diversification. In Procs. of WWW, 2005.

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  • (2021)Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex NetworksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.31008898:3(2613-2624)Online publication date: 1-Jul-2021
  • (2021)Academic Influence Aware and Multidimensional Network Analysis for Research Collaboration Navigation Based on Scholarly Big DataIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2018.28600519:1(246-257)Online publication date: 1-Jan-2021
  • (2021)A framework for inventor collaboration recommendation system based on network approachExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114833176:COnline publication date: 15-Aug-2021
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  1. Using link semantics to recommend collaborations in academic social networks

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    Published In

    cover image ACM Other conferences
    WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web
    May 2013
    1636 pages
    ISBN:9781450320382
    DOI:10.1145/2487788

    Sponsors

    • NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
    • CGIBR: Comite Gestor da Internet no Brazil

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 13 May 2013

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    Author Tags

    1. collaboration recommendation
    2. link prediction
    3. social network

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    • Research-article

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    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%

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    Cited By

    View all
    • (2021)Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex NetworksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.31008898:3(2613-2624)Online publication date: 1-Jul-2021
    • (2021)Academic Influence Aware and Multidimensional Network Analysis for Research Collaboration Navigation Based on Scholarly Big DataIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2018.28600519:1(246-257)Online publication date: 1-Jan-2021
    • (2021)A framework for inventor collaboration recommendation system based on network approachExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114833176:COnline publication date: 15-Aug-2021
    • (2021)Network Approach for Inventor Collaboration Recommendation SystemSecond International Conference on Networks and Advances in Computational Technologies10.1007/978-3-030-49500-8_8(83-93)Online publication date: 3-Feb-2021
    • (2021)Proximity‐aware research leadership recommendation in research collaboration via deep neural networksJournal of the Association for Information Science and Technology10.1002/asi.2454673:1(70-89)Online publication date: 6-Dec-2021
    • (2020)HeteroRWR: A Novel Algorithm for Top-k Co-Author Recommendation with Fusion of Citation NetworksIEICE Transactions on Information and Systems10.1587/transinf.2019EDP7108E103.D:1(71-84)Online publication date: 1-Jan-2020
    • (2020)Flows and PatternsFundamental Theories of Business Communication10.1007/978-3-030-57741-4_7(95-117)Online publication date: 5-Dec-2020
    • (2019)AgreeRelTrust—A Simple Implicit Trust Inference Model for Memory-Based Collaborative Filtering Recommendation SystemsElectronics10.3390/electronics80404278:4(427)Online publication date: 11-Apr-2019
    • (2019)A System for Discovery of Knowledge in Data Repository EducationInternational Journal of Information and Education Technology10.18178/ijiet.2019.9.8.12619:8(535-538)Online publication date: 2019
    • (2018)Visualizing Co-Authorship Social Networks and Collaboration Recommendations With CNAReGraph Theoretic Approaches for Analyzing Large-Scale Social Networks10.4018/978-1-5225-2814-2.ch011(173-188)Online publication date: 2018
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