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| An Approach to Identifying Transformative Research by Integrating Citation Function and Triangular Citation Structure |
| Zheng Zhejun1,2, Ma Yaxue1,2, Liang Zhentao3, Bai Yun3, Pei Lei1,2 |
1.Laboratory of Data Intelligence and Interdisciplinary Innovation, Nanjing University, Nanjing 210023
2.School of Information Management, Nanjing University, Nanjing 210023
3.School of Information Management, Wuhan University, Wuhan 430072 |
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Abstract Transformative research (TR) serves as a precursor to the emergence of new paradigms or disciplines in science and technology. Identifying TR is important for R&D management and technological forecasting. In response to the gaps in considering how citations of different functions impact the evaluation of focal papers, this study proposed a novel approach to identifying TR by integrating the citation function and triangular citation structure. By obtaining the triangular citation structure among the focal paper, its predecessor, and its successor based on different combinations of citation functions, we extracted the relationships of consolidation or disruption between the papers. An egocentric consolidation-disruption citation network (ECCD) was constructed for each focal paper. The ECCD network structure and text input were employed to build a heterogeneous graph attention neural network model, which was used to identify TR that possessed both high academic impact and peer-reviewed disruptive papers. An empirical analysis of the PubMed Central dataset Open Access subset revealed that the TR identification task achieved an optimal F1-score of 0.3926, which exceeded that of other baseline models. Further parameter analysis showed that disruptive citations interpreted by the citation function played a critical role in identifying high-impact and transformative research.
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Received: 23 October 2024
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