Clustering-based link prediction in scientific coauthorship networks

Yang Ma (), Guangquan Cheng, Zhong Liu () and Xingxing Liang ()
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Yang Ma: Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, P. R. China
Guangquan Cheng: Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, P. R. China
Zhong Liu: Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, P. R. China
Xingxing Liang: Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, P. R. China

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 06, 1-12

Abstract: Link prediction in social networks has become a growing concern among researchers. In this paper, the clustering method was used to exploit the grouping tendency of nodes, and a clustering index (CI) was proposed to predict potential links with characteristics of scientific cooperation network taken into consideration. Results showed that CI performed better than the traditional indices for scientific coauthorship networks by compensating for their disadvantages. Compared with traditional algorithms, this method for a specific type of network can better reflect the features of the network and achieve more accurate predictions.

Keywords: Complex networks; spectral clustering; link prediction; scientific coauthorship (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1142/S0129183117500826

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