Applied Intelligence

, Volume 48, Issue 2, pp 465–481 | Cite as

An evolutionary non-linear ranking algorithm for ranking scientific collaborations

  • Fahimeh Ghasemian
  • Kamran Zamanifar
  • Nasser Ghasem-Aghaee


The social capital theory motivates some researchers to apply link-based ranking algorithms (e.g. PageRank) to compute the fitness level of a scholar for collaborating with other scholars on a set of skills. These algorithms are executed on the collaboration network of scholars and assign a score to each scholar based on the scores of his/her neighbors by solving a linear system in an iterative way. In this paper, we propose a new ranking algorithm by focusing on link-aggregation function and transition matrix. The evolution strategy technique is applied to find the best aggregation function and transition matrix for computing the score of a scholar in the collaboration network which is modeled by a hypergraph. Experiments conducted on two datasets gathered from ScivalExpert and VIVO show that the new non-linear ranking algorithm acts better than the other iterative ranking approaches for ranking scientific collaborations.


Scientific collaboration Hypergraph Ranking algorithm Evolution strategy Collaboration network 


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Fahimeh Ghasemian
    • 1
  • Kamran Zamanifar
    • 1
  • Nasser Ghasem-Aghaee
    • 1
  1. 1.Department of Software Engineering, Faculty of Computer EngineeringUniversity of IsfahanIsfahanIran

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