inis 20(23): e3

Research Article

A Comprehensive Survey of Link Prediction Techniques for Social Network

Download76 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/eai.13-7-2018.163988,
    author={Abdul Samad and Mamoona Qadir and Ishrat Nawaz and Muhammad Arshad Islam and Muhammad Aleem},
    title={A Comprehensive Survey of Link Prediction Techniques for Social Network},
    journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
    volume={7},
    number={23},
    publisher={EAI},
    journal_a={INIS},
    year={2020},
    month={4},
    keywords={Link Prediction, Social Network, Survey},
    doi={10.4108/eai.13-7-2018.163988}
}
  • Abdul Samad
    Mamoona Qadir
    Ishrat Nawaz
    Muhammad Arshad Islam
    Muhammad Aleem
    Year: 2020
    A Comprehensive Survey of Link Prediction Techniques for Social Network
    INIS
    EAI
    DOI: 10.4108/eai.13-7-2018.163988
Abdul Samad1,*, Mamoona Qadir2, Ishrat Nawaz3, Muhammad Arshad Islam4, Muhammad Aleem4
  • 1: Capital University of Science and Technology, Islamabad Pakistan
  • 2: Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan Pakistan
  • 3: The Islamia University of Bahawalpur, Bahawalpur Pakistan
  • 4: FAST-National University of Computer and Emerging Sciences, Islamabad Pakistan
*Contact email: writetosamadalvi@gmail.com

Abstract

A growing trend of using social networking sites is attracting researchers to study and analyze different aspects of social network. Besides many problems, link prediction is a fascinating problem in the field of social network analysis (SNA). Link prediction, in social network analysis, is a task of identifying the missing links and predicting the new links. Several researchers have proposed solutions for the link prediction problem during the past two decades. However, there is a need to provide comprehensive overview of the significant contributions for a thorough analysis. The objective of this review is to summaries and discuss the existing link prediction algorithms in a common context for an unbiased analysis. The extensive review is presented by constructing the systematical category for proposed algorithms, selected problems, evaluation measures along with selected network datasets. Finally, applications of link prediction are discussed.

Keywords
Link Prediction, Social Network, Survey
Received
2020-01-28 00:00:00 UTC
Accepted
2020-04-16 00:00:00 UTC
Published
2020-04-17
Publisher
EAI
http://dx.doi.org/10.4108/eai.13-7-2018.163988

Copyright © 2020 Abdul Samad, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.