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| Predicting Research Collaborations Based on Network Embedding |
| Zhang Jinzhu, Yu Wenqian, Liu Jingjie, Wang Yue |
| Department of Information Management, School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094 |
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Abstract In order to improve the efficiency and effect of predicting research collaborations in a large data environment, correlations among researchers should be learned and discovered automatically from massive datasets. Firstly, the co-authorship network is built from a massive dataset where research collaborations are indicated by co-authorship. Then, the researchers’ context in the network is learned by network embedding based on the deep machine learning method, and each researcher’s dense, low-dimensional vector is formatted. Finally, the semantic similarities among authors are calculated through the vector similarity indices for research collaboration prediction. Experiments in the field of Library and Information Science verify that the method can improve the accuracy and efficiency of research collaboration prediction. This method enriches and expands the information analysis methods based on complex networks from the perspective of data science.
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Received: 24 April 2017
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