Co-authorship Network Embedding and Recommending Collaborators via Network Embedding

  • Ilya MakarovEmail author
  • Olga Gerasimova
  • Pavel Sulimov
  • Leonid E. Zhukov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11179)


Co-authorship networks contain invisible patterns of collaboration among researchers. The process of writing joint paper can depend of different factors, such as friendship, common interests, and policy of university. We show that, having a temporal co-authorship network, it is possible to predict future publications. We solve the problem of recommending collaborators from the point of link prediction using graph embedding, obtained from co-authorship network. We run experiments on data from HSE publications graph and compare it with relevant models.


Co-authorship network Graph embedding Machine learning Recommender system 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Data Analysis and Artificial IntelligenceNational Research University Higher School of EconomicsMoscowRussia
  2. 2.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia

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