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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (1): 104-117    DOI: 10.11925/infotech.2096-3467.2018.0394
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Finding Collaboration Opportunities from Emerging Issues with LDA Topic Model and Link Prediction
Junwan Liu(),Zhixin Long,Feifei Wang
School of Economics and Management, Beijing University of Technology, Beijing 100022, China
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Abstract  

[Objective] This paper proposes a new method to discover collaboration opportunities from emerging issues. [Methods] We used literature corpus of deep learning as the research object. Firstly, we explored the intrinsic characteristics of these literature with the LDA topic model. Then, we calculated their weights, and used topics as nodes to build topic co-occurrence network. Finally, we applied link prediction to find the potential opportunities. [Results] The optimal index of topic co-occurrence network in deep learning was AA. The big data analysis research in deep learning were more likely associated with the biomedical studies and the improvement of related algorithms. [Limitations] Link prediction generated poor results for badly connected networks. [Conclusions] The LDA topic model and link prediction method could help us find new collaboration opportunities from emerging issues.
Key words： Emerging Topic Association      LDA Topic Model      Link Prediction     
Received: 09 April 2018      Published: 04 March 2019
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Junwan Liu
Zhixin Long
Feifei Wang

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Junwan Liu,Zhixin Long,Feifei Wang. Finding Collaboration Opportunities from Emerging Issues with LDA Topic Model and Link Prediction. Data Analysis and Knowledge Discovery, 2019, 3(1): 104-117.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0394     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I1/104

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