Improving Relational Classification Using Link Prediction Techniques

  • Cristina Pérez-Solà
  • Jordi Herrera-Joancomartí
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8188)


In this paper, we address the problem of classifying entities belonging to networked datasets. We show that assortativity is positively correlated with classification performance and how we are able to improve classification accuracy by increasing the assortativity of the network. Our method to increase assortativity is based on modifying the weights of the edges using a scoring function. We evaluate the ability of different functions to serve for this purpose. Experimental results show that, for the appropriated functions, classification on networks with modified weights outperforms the classification using the original weights.


Class Label Online Social Network Preferential Attachment Original Graph Link Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Cristina Pérez-Solà
    • 1
  • Jordi Herrera-Joancomartí
    • 1
    • 2
  1. 1.Dept. d’Enginyeria de la Informació i les ComunicacionsUniversitat Autònoma de BarcelonaBellaterraSpain
  2. 2.Internet Interdisciplinary Institute (IN3)UOCSpain

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