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| Identifying Emerging Topics in Disciplines by Integrating Network Structural Features |
| Yang Jinqing1, Luo Man1, Cheng Xiufeng1, Xia Lixin1, Ma Tingcan2,3 |
1.School of Information Management, Central China Normal University, Wuhan 430079
2.National Science Library (Wuhan), Chinese Academy of Sciences, Wuhan 430071
3.Hubei Key Laboratory of Big Data in Science and Technology, Wuhan 430071 |
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Abstract Identifying emerging topics in disciplines is an effective method in the advancement of technological innovation and tracking trends in disciplinary development. The emergence of emerging topics in disciplines is a complex process influenced by both scientific communication and self-organizing networks, whereby the network structures of emerging topics in different disciplines acquire unique properties. Based on the common characteristics of scientific communication, this study integrated the global and local network structural features of disciplinary topics. A standard experimental dataset was generated by random matching. A multi-indicator weighted fusion method and a machine learning classification method were then used to identify emerging topics in the disciplines. The results indicate that the multi-indicator weighted fusion method is effective in identifying top-ranked influential disciplinary topics. However, the P@270 high-influence topics accounted for only 60%, which is lower than the optimal performance of the random forest classification model of 64.14%. This indicates that the machine learning classification method has a greater advantage in fitting complex processes, whereas the multi-indicator weighted fusion method is more suitable for tasks focusing on the most influential topics. The results of machine learning interpretability analysis show that a higher citation frequency, network influence, number of published papers, and higher influence of authors on journals contribute to the identification of high-influence topics, whereas a higher degree of structural mutation has a negative effect on identifying emerging topics in disciplines.
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Received: 02 September 2024
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