Link prediction in paper citation network to construct paper correlation graph

Nowadays, recommender system has become one of the main tools to search for users’ interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user’s need of keywords. Besides, to satisfy the user...

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Bibliographic Details
Published in:EURASIP journal on wireless communications and networking Vol. 2019; no. 1; pp. 1 - 12
Main Authors: Liu, Hanwen, Kou, Huaizhen, Yan, Chao, Qi, Lianyong
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 16.10.2019
Springer Nature B.V
SpringerOpen
Subjects:
Authors’ information
Citation analysis
Communications Engineering
Correlation
Engineering
Information Systems Applications (incl.Internet)
Keywords
Link prediction
Multi-modal Sensor Data Fusion in Internet of Things
Networks
Paper citation network
Paper correlated graph
Recommender systems
Signal,Image and Speech Processing
Time
User requirements
User satisfaction
Link prediction
Authors’ information
Time
Keywords
Paper correlated graph
Paper citation network
ISSN:1687-1499, 1687-1472, 1687-1499
Online Access:Get full text
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Summary:Nowadays, recommender system has become one of the main tools to search for users’ interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user’s need of keywords. Besides, to satisfy the users’ requirements of further research on a certain domain, the recommended papers must be correlated. However, each paper of an existing paper citation network hardly has cited relationships with others, so the correlated links among papers are very sparse. In addition, while a mass of research approaches have been put forward in terms of link prediction to address the network sparsity problems, these approaches have no relationship with the effect of self-citations and the potential correlations among papers (i.e., these correlated relationships are not included in the paper citation network as their published time is close). Therefore, we propose a link prediction approach that combines time, keywords, and authors’ information and optimizes the existing paper citation network. Finally, a number of experiments are performed on the real-world Hep-Th datasets. The experimental results demonstrate the feasibility of our proposal and achieve good performance.
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ISSN:1687-1499
1687-1472
1687-1499
DOI:10.1186/s13638-019-1561-7