Who will cite you back? Reciprocal link prediction in citation networks. Issue 4 (20th November 2017)

• Record Type: Journal Article
• Authors: Daud, Ali; Ahmed, Waqas; Amjad, Tehmina; Nasir, Jamal Abdul; Aljohani, Naif Radi; Abbasi, Rabeeh Ayaz; Ahmad, Ishfaq
• Is Part Of: Library hi tech. Volume 35:Issue 4(2017)
• Page Start: 509
• Page End: 520
• Publication Date: 2017-11-20
• Subjects: Digital libraries -- Decision making -- Classification -- Data mining -- Communities -- Library networks -- Link prediction -- Reciprocal links -- Social network mining; Library science -- Technological innovations -- Periodicals; Libraries -- Automation -- Periodicals; Information science -- Periodicals; 025.00285
• DOI: 10.1108/LHT-02-2017-0044
• Languages: English
• ISSNs: 0737-8831

Abstract

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: Extensive experiments are performed on CiteSeer data set by using three classification algorithms (decision trees, Naive Bayes, and support vector machines) to analyze the impact of individual, category wise and combination of features. The results reveal that it is likely to precisely predict 96 percent of reciprocal links. The study delivers convincing evidence of presence of the underlying equilibrium amongst 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. Originality/value: The proposed method uses reciprocal link prediction for bibliographic networks in a novel way.