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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (4): 38-45    DOI: 10.11925/infotech.2096-3467.2017.04.05
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Recommending Scientific Research Collaborators with Link Prediction and Extremely Randomized Trees Algorithm
Weimin Lv1,2,Xiaomei Wang3(),Tao Han1
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China
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Abstract  

[Objective] This paper proposes a method to recommend scientific research collaborators based on link prediction and machine learning, which improves the precision of traditional method. [Methods] First, we used Link Prediction Algorithm index to build the feature input, and adopted the Extremely Randomized Trees Algorithm to train the classifier. Then, we obtained the optimal weight combination with the traversal algorithm to combine the classification results linearly. Finally, we received the best recommendation of collaborators. [Results] The improved ET method had better performance than the existing ones in recommending the collaboration cities. Besides, the proposed method was less affected by factors such as the network structure, and could be used with more applications. [Limitations] Scientific research collaboration is affected by the cooperation motivation, geographical, language and many other factors. The weighted author network did not examine authors from the same cities or with the same organizations. [Conclusions] The propsoed method could produce better recommendation results, which might help universities, institutions and individuals identify academic collabortors.

Key wordsScientific Research Collaboration Network      Link Prediction      Machine Learning      Random Forest      Extremely Randomized Trees      Recommendation     
Received: 16 January 2017      Published: 24 May 2017

Cite this article:

Weimin Lv,Xiaomei Wang,Tao Han. Recommending Scientific Research Collaborators with Link Prediction and Extremely Randomized Trees Algorithm. Data Analysis and Knowledge Discovery, 2017, 1(4): 38-45.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.04.05     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I4/38

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