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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (9): 49-56    DOI: 10.11925/infotech.2096-3467.2017.09.05
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Detecting Community in Scientific Collaboration Network with Bayesian Symmetric NMF
Xiaohua Shi1,2(),Hongtao Lu2
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.

Key wordsScientific Network      Co-author Network      Community Detection      Non-negative Matrix Factorization      Bayesian Approach     
Received: 10 April 2017      Published: 18 October 2017

Cite this article:

Xiaohua Shi,Hongtao Lu. Detecting Community in Scientific Collaboration Network with Bayesian Symmetric NMF. Data Analysis and Knowledge Discovery, 2017, 1(9): 49-56.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.09.05     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I9/49

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