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Long Distance In-Links for Ranking Enhancement

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Intelligent Distributed Computing XII (IDC 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 798))

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Abstract

Ranking is a widely used technique to classify nodes in networks according to their relevance. Increasing one’s rank is a desiderable feature in almost any context; several approaches have been proposed to achieve this goal by exploiting in-links and/or out-links with other existing nodes. In this paper, we focus on the impact of in-links in rank improvement (with PageRank metric) and their distance from starting link. Results for different networks both in type and size show that the best improvement comes from long distance nodes rather than neighbours, somehow subverting the commonly adopted social-based approach.

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Author information

Authors and Affiliations

  1. Dip. Ingegneria Elettrica, Elettronica e Informatica, Università degli Studi di Catania, Catania, Italy

    V. Carchiolo, M. Grassia, A. Longheu, M. Malgeri & G. Mangioni

Authors
  1. V. Carchiolo
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  2. M. Grassia
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  3. A. Longheu
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  4. M. Malgeri
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  5. G. Mangioni
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Corresponding author

Correspondence to G. Mangioni .

Editor information

Editors and Affiliations

  1. Basque Center for Applied Mathematics (BCAM), TECNALIA, University of the Basque Country (UPV/EHU), Derio, Bizkaia, Spain

    Javier Del Ser

  2. TECNALIA, Derio, Bizkaia, Spain

    Eneko Osaba

  3. University of the Basque Country (UPV/EHU), Bilbao, Bizkaia, Spain

    Miren Nekane Bilbao

  4. Computer Science Department, University of Las Palmas de Gran Canaria, Las Palmas, Spain

    Javier J. Sanchez-Medina

  5. OpenIoT Reesarch Unit, FBK CREATE-NET, Trento, Italy

    Massimo Vecchio

  6. Department of Design Engineering and Mathematics, Middlesex University, London, UK

    Xin-She Yang

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Carchiolo, V., Grassia, M., Longheu, A., Malgeri, M., Mangioni, G. (2018). Long Distance In-Links for Ranking Enhancement. In: Del Ser, J., Osaba, E., Bilbao, M., Sanchez-Medina, J., Vecchio, M., Yang, XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-99626-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-99626-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99625-7

  • Online ISBN: 978-3-319-99626-4

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