Current trends and challenges in link prediction methods in dynamic social networks: A literature review

Elfadil Abdalla Mohamed, Nazar Zaki, Mohammad Marjan

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In more recent times, researchers have turned their attention to link prediction and the role link inference can play in better understanding the evolutionary nature of social networking sites. The objective of this paper is to present an in-depth review, analysis, and discussion of the cutting-edge link prediction methods that can be applied to better understand the development of social networks. The findings of the literature review reveal that there has been a steady increase in the number of published articles that present novel link prediction models that are designed to enhance the efficiency and accuracy of link prediction. In this paper, this most recent techniques to be proposed in this regard are compared and categorized, and features and evaluation metrics are presented for each approach. The results of the evaluation reveal that there are no complete or definitive methods available that can accurately and reliably be applied within different dynamic social networks to predict missing, emerging, and broken links within the network. The paper concludes by presenting potential future directions and recommendations for further studies.

Original languageEnglish
Pages (from-to)244-254
Number of pages11
JournalAdvances in Science, Technology and Engineering Systems
Issue number6
Publication statusPublished - 2019


  • Dynamic social networks
  • Link inference
  • Link prediction

ASJC Scopus subject areas

  • Engineering (miscellaneous)
  • Physics and Astronomy (miscellaneous)
  • Management of Technology and Innovation


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