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| Network Analysis of Inter-Division in Funding Projects: Case Studies of AI Field in NSF Data |
| Yang Jie1, Wang Yuefen1,2,3, Chen Bikun4, Hui Guangping1 |
1.School of Economics & Management, Nanjing University of Science & Technology, Nanjing 210094
2.Management School of Tianjin Normal University, Tianjin 300387
3.Institute for Big Data Science, Tianjin Normal University, Tianjin 300387
4.School of Social Science, Soochow University, Suzhou 215021 |
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Abstract This study aims to explore the funding direction, influence, characters, and evolution of a crossing study from the perspective of division by analyzing the research fusion and trends of funding projects. A co-word network analysis is combined with interdisciplinary research methods to construct an analysis framework and a measurement principle with an aim to discover the development of the inner-division knowledge and the inter-division research trends from the dimensions of the inner-division compactness of co-word networks and the intersection of inter-division knowledge. The results show that, over time, as per the National Science Foundation (NSF) data, both the number of projects and the diversity of topics in the different divisions of the artificial intelligence (AI) field are increasing. Further, from the two dimensions, the degree of inner-division compactness keeps decreasing while the intersection of inter-division knowledge intensifies, and there exists an obvious polarization between them. Moreover, the intersecting content of NSF’s different divisions is concentrated in the topics with the same or similar theoretical methods, which indicates that it is easier to realize cross-integration for similar knowledge, and there exists some relationship between the inner-division compactness of co-word networks and the crossing and integration of inter-division knowledge.
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Received: 22 July 2021
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