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Link Prediction in Complex Networks Based on Cluster Information

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Advances in Artificial Intelligence - SBIA 2012 (SBIA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7589))

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Abstract

Cluster in graphs is densely connected group of vertices sparsely connected to other groups. Hence, for prediction of a future link between a pair of vertices, these vertices common neighbors may play different roles depending on if they belong or not to the same cluster. Based on that, we propose a new measure (WIC) for link prediction between a pair of vertices considering the sets of their intra-cluster or within-cluster (W) and between-cluster or inter-cluster (IC) common neighbors. Also, we propose a set of measures, referred to as W forms, using only the set given by the within-cluster common neighbors instead of using the set of all common neighbors as usually considered in the basic local similarity measures. Consequently, a previous clustering scheme must be applied on the graph. Using three different clustering algorithms, we compared WIC measure with ten basic local similarity measures and their counterpart W forms on ten real networks. Our analyses suggest that clustering information, no matter the clustering algorithm used, improves link prediction accuracy.

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Author information

Authors and Affiliations

  1. Departamento de Ciências de Computação Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Campus de São Carlos, Caixa Postal 668, 13560-970, São Carlos, SP, Brazil

    Jorge Carlos Valverde-Rebaza & Alneu de Andrade Lopes

Authors
  1. Jorge Carlos Valverde-Rebaza
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  2. Alneu de Andrade Lopes
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Editor information

Editors and Affiliations

  1. IME, Department of Computer Science, University of São Paulo, Brazil

    Leliane N. Barros

  2. IME, Department of Computer Science, University of São Paulo, São Paulo, Brazil

    Marcelo Finger

  3. DInf - Federal University of Paraná, CEP 19031-970, Curitiba, Brazil

    Aurora T. Pozo

  4. Federal University of Technology, Paraná, Brazil

    Gustavo A. Gimenénez-Lugo

  5. Department of Inforumatics, Federal University of Paraná, Curitiba, Brazil

    Marcos Castilho

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© 2012 Springer-Verlag Berlin Heidelberg

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Valverde-Rebaza, J.C., de Andrade Lopes, A. (2012). Link Prediction in Complex Networks Based on Cluster Information. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds) Advances in Artificial Intelligence - SBIA 2012. SBIA 2012. Lecture Notes in Computer Science(), vol 7589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34459-6_10

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  • DOI: https://doi.org/10.1007/978-3-642-34459-6_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34458-9

  • Online ISBN: 978-3-642-34459-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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