Semantic and Event-Based Approach for Link Prediction

  • Till Wohlfarth
  • Ryutaro Ichise
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5345)


The scientific breakthroughs resulting from the collaborations between researchers often outperform the expectations. But finding the partners who will bring this synergic effect can take time and sometime gets nowhere considering the huge amounts of experts in various disciplines. We propose to build a link predictor in a network where nodes represent researchers and links - coauthorships. In this method we use the structure of the constructed graph, and propose to add a semantic and event based approach to improve the accuracy of the predictor. In this case, predictors might offer good suggestions for future collaborations. We will be able to compute the classification of a massive dataset in a reasonable time by under-sampling and balancing the data. This model could be extended in other fields where the research of partnership is important as in world of institutions, associations or companies. We believe that it could also help with finding communities of topics, since link predictors contain implicit information about the semantic relation between researchers.


Feature Vector Collaboration Network Minority Class Link Prediction Common Neighbour 
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 2008

Authors and Affiliations

  • Till Wohlfarth
    • 1
    • 2
  • Ryutaro Ichise
    • 2
  1. 1.University Paris VIParisFrance
  2. 2.National Institute of InformaticsTokyoJapan

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