Who will cite you back? Reciprocal link prediction in citation networks
| Pages | 509-520 |
| Published date | 20 November 2017 |
| DOI | https://doi.org/10.1108/LHT-02-2017-0044 |
| Date | 20 November 2017 |
| Author | Ali Daud,Waqas Ahmed,Tehmina Amjad,Jamal Abdul Nasir,Naif Radi Aljohani,Rabeeh Ayaz Abbasi,Ishfaq Ahmad |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Information user studies,Metadata,Information & knowledge management,Information & communications technology,Internet |
Who will cite you back?
Reciprocal link prediction in
citation networks
Ali Daud
Department of Computer Science and Software Engineering,
International Islamic University, Islamabad, Pakistan and
Department of Information Systems,
Faculty of Computing and Information Technology, King Abdulaziz University,
Jeddah, Saudi Arabia
Waqas Ahmed
Department of Computer Science and Software Engineering,
International Islamic University, Islamabad, Pakistan
Tehmina Amjad and Jamal Abdul Nasir
Department of Computer Science and Software Engineering,
International Islamic University, Islamabad, Pakistan
Naif Radi Aljohani
Faculty of Computing and Information Technology, King Abdulaziz University,
Jeddah, Saudi Arabia
Rabeeh Ayaz Abbasi
Department of Information Systems,
Faculty of Computing and Information Technology, King Abdulaziz University,
Jeddah, Saudi Arabia and
Department of Computer Science, Quaid-i-Azam University,
Islamabad, Pakistan, and
Ishfaq Ahmad
Department of Mathematics and Statistics, International Islamic University,
Islamabad, Pakistan
Abstract
Purpose –Link prediction in social networks refers toward inferring the new interactions among the users in
near future. Citation networks are constructed based on citing each other papers. Reciprocal link prediction in
citations networks refers toward inferring about getting a citation from an author, whose work is already
cited by you. The paper aims to discuss these issues.
Design/methodology/approach –In this paper, the authors study the extent to which the information of a
two-way citation relationship (called reciprocal) is predictable. The authors propose seven different features
based on papers, their authors and citations of each paper to predict reciprocal links.
Findings –Extensiveexperiments are performed on CiteSeerdata set by using three classificationalgorithms
(decision trees, NaiveBayes, and support vector machines) to analyzethe impact of individual, category wise
and combinationof features. The results revealthat it is likely to precisely predict 96 percentof reciprocal links.
The study deliversconvincing evidence of presence of the underlying equilibriumamongst reciprocal links.
Research limitations/implications –It is not a generic method for link prediction which can work for
different networks with relevant features and parameters.
Practical implications –This paper predicts the reciprocal links to show who is citing your work to
collaborate with them in future.
Social implications –The proposed method will be helpful in finding collaborators and developing
academic links.
Library Hi Tech
Vol. 35 No. 4, 2017
pp. 509-520
© Emerald PublishingLimited
0737-8831
DOI 10.1108/LHT-02-2017-0044
Received 21 February 2017
Revised 13 April 2017
Accepted 26 May 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0737-8831.htm
509
Reciprocal link
prediction
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