Detecting the emergence of new scientific collaboration links in Africa: A comparison of expected and realized collaboration intensities

R. Guns*, L. Wang

*Corresponding author for this work

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The evolution of a collaboration network is to some extent steered by the network topology itself. This is the reason behind the success of network evolution models and approaches to link prediction. At the same time, some changes are due to exogenous factors (i.e., factors external to the network itself). In this paper, we explore changes in the collaboration network of African countries (2000-2014), with a focus on detecting emergent links beyond endogenous network features. Using link prediction and machine learning, we generate an 'expected' (predicted) collaboration network based on past data and compare it with the actual network that evolved in later years. The results show that the intensity of collaboration with non-African countries is higher than expected, especially for countries that are scientifically more active. To a lesser extent, we also find an increase in collaboration within Africa, which seems mostly due to the scientifically less developed countries. Emergent collaborations are mostly found in the first half of the studied period; in the second half the network structure appears to stabilize. (C) 2017 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)892-903
Number of pages12
JournalJournal of Informetrics
Issue number3
Publication statusPublished - Aug 2017

JEL classifications

  • o32 - Management of Technological Innovation and R&D
  • o30 - "Technological Change; Research and Development; Intellectual Property Rights: General"
  • o55 - Economywide Country Studies: Africa


  • Africa
  • Collaboration
  • Geographic groups
  • Link prediction
  • Networks
  • Outside-Africa collaboration
  • Scientific research
  • Within-Africa collaboration
  • Learning systems
  • Networks (circuits)
  • Scientific researches
  • Forecasting
  • WEB

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