Link Prediction in Dynamic Social Networks: A Literature Review

Mohammad Marjan, Nazar Zaki, Elfadil A. Mohamed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Social network link prediction has gained significant attention and become a key research focus over the last two decades. The prediction of missing links in the current network and emerging or broken links in future networks is essential for the understanding of their evolutionary nature. Social networks are changing dynamically over time. Link inference in dynamic social networks is an extremely challenging process and few link prediction methods consider their evolving nature. The aim of this paper is to comprehensively review, analyze, discuss and evaluate state-of-the-art link prediction methods in dynamic social networks. The leading link prediction methods and techniques that network science has produced are categorized and compared. Features and evaluation metrics for each method are presented. Finally, some future directions and recommendations are provided.

Original languageEnglish
Title of host publication5th International Congress on Information Science and Technology, CiSt 2018
EditorsMohammed Al Achhab, Mohammed El Mohajir, Ismail Jellouli, Badr Eddine El Mohajir
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages200-207
Number of pages8
ISBN (Electronic)9781538643853
DOIs
Publication statusPublished - Dec 28 2018
Event5th International Congress on Information Science and Technology, CiSt 2018 - Marrakech, Morocco
Duration: Oct 22 2018Oct 24 2018

Publication series

NameColloquium in Information Science and Technology, CIST
Volume2018-October
ISSN (Print)2327-185X
ISSN (Electronic)2327-1884

Conference

Conference5th International Congress on Information Science and Technology, CiSt 2018
Country/TerritoryMorocco
CityMarrakech
Period10/22/1810/24/18

Keywords

  • dynamic social etworks
  • evolutionary
  • link inference
  • link prediction

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing
  • Information Systems and Management
  • Management Science and Operations Research

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