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| Identifying Emerging Scientific Topics by Abrupt Change of Knowledge Structure |
| Duan Qingfeng, Chen Hong, Yan Xuxian, Liu Dongxia |
| School of Management Science & Engineering, Shanxi University of Finance & Economics, Taiyuan 030006 |
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Abstract Understanding the structural transformation of knowledge is key to identify emerging scientific topics. Novel distribution of knowledge elements implies that emerging topics occur, and transformation of knowledge structure has become the characteristic to identify emerging topics. Following these ideas, we measured the difference of structure between two knowledge networks in sequential time using the WL subtree kernel and propose an indicator in terms of structure transformation of knowledge to suggest the extent to which a new topic emerges. In addition, we propose another indicator that measures the growth rate of a topic’s influence over time using the PageRank algorithm. Then, these two indicators together compose a two-dimensional space for identification and form an integrated solution based on the dynamic topology of the knowledge network. The empirical research in the field of information science well-validated our method, with high sensitivity and effective discrimination towards emerging topics with high value in the short term. These topological transformation-based indicators are capable of quantitatively analyzing the evolution of academic topics and offer a unique view of knowledge structure to enable scientific evolution.
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Received: 29 August 2022
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