Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data

Understanding the complex patterns in research funding plays a fundamental role in comprehensively revealing funding preferences and informing ideas for future strategic innovation. This is especially true when the funding policies need to be constantly shifted to accommodate highly complex and ever-changing demands for technological, economic, and social development. To this end, we investigate the associations between funding agencies and the topics they fund in an attempt to understand funding patterns at both an organizational level and a topic level. In this paper, the links between heterogeneous nodes, organizations and topics, are mapped to a two-mode organization–topic network. The collaborative interactions formed by funding organizations and the semantic networks constituted by word embedding-enhanced topics are revealed and analyzed simultaneously. The methodology is demonstrated through a case study on big data research involving 9882 articles from the Web of Science over the period 2010 to 2019. The result shows a comprehensive picture of the topics that governments, academic institutions, and industrial funding organizations prefer to fund, which provide potential decision support for agencies and organizations who are exploring funding patterns, estimating funding trends, and updating their funding strategies.


Author(s): Qianqian Jin, Hongshu Chen, Ximeng Wang, Tingting Ma, Fei Xiong
Organization(s): Beijing Institute of Technology, Beijing Wuzi University, Beijing Jiaotong University
Source: Scientometrics
Year: 2022

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