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Detecting Community in Scientific Collaboration Network with Bayesian Symmetric NMF |
Shi Xiaohua1,2(), Lu Hongtao2 |
1Library of Shanghai Jiaotong University, Shanghai 200240, China 2Computer Science Department, Shanghai Jiaotong University, Shanghai 200240, China |
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Abstract [Objective] This study proposes and examines a new method to identify the communities in collaboration network of scientific researchers. [Methods] First, we retrieved the need data from information science journal articles published from 2012 to 2016. Then, we used the Automatic Relevance Determination to find the target community with the Bayesian Symmetric Non-negative Matrix Factorization method. Finally, we compared the performance of our method with the existing ones. [Results] The proposed method got better results than others. [Limitations] Did not optimize our data with the researcher identifications. [Conclusions] The proposed method could effectively find communities from the scientific collaboration network.
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Received: 10 April 2017
Published: 18 October 2017
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