Abstract
The influence of authors is mostly based on their capacity to form specific self-contained and/or active research communities or topics while also inspiring fruitful spin-off research derived from those communities or topics. Accurately estimating author influence and its indirect effect in inspiring external research creativity must thus be based on very precise community roles as well as topic-role analysis on citing material. In this paper we thus implement, compare, and combine two different kinds of knowledge mapping approaches used to track authors’ influence over time—a recent topic-based mapping approach and an original hybrid community-based mapping approach. The experimental context of our study is the analysis of the dynamics and of the influence of the research of Prof. Liu Zeyuan, the most important contributor in the field of Science of Science in China. This analysis focuses on papers citing Professor Liu Zeyuan's research and highlights the full extent of his creative thinking.
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Notes
We only considered papers for which an English title is provided.
Full disambiguation of Chinese names is a complex problem that is not solved here. Hence, different Chinese names might well have the same English spelling even though they refer to different persons.
In this article, the features represent the keywords extracted from the title and the abstract of the papers, the weights of the features are the adjusted frequency information associated with them and the unsupervised classification (clustering) is based on the GNG algorithm (Fritzke, 1995).
A feature with negative values can be separated into 2 different positive sub-features without loss of information. The first represents the positive part of the original feature and the second its negative part.
In Eq. 7, labels represent categories or clusters to which data are associated.
The GNG clustering is a crisp clustering process. Consequently, each original data (here the citing paper) is assigned to one single cluster.
The main visualization process is operated with Dash web application library, the overall management of network construction is dealt with Networkx library and the specific operations on communities are tackled with Community library.
In all our experiments the chosen threshold is 3.
The initial grouping of clusters into meta-topics was obtained using an expert analysis based on the evaluation of the similarity between clusters’ content (i.e., similarity between clusters’ most contrasted terms) as well as on external categorization of science of science into main domains as the one presented in (Lamirel et al. 2000). However, the groups obtained could also have been extracted by simply looking at the results of graph visualization approaches presented in section ″Contrast graph of the research topics of papers citing Liu’s research″ and ″Hybrid community-topic 3D-graph of the research topics of papers citing Liu’s research″ but these methods certainly prove to provide an inherent equivalent structuring.
From 2009, 5 training seminars on knowledge mapping were hold at WISELAB (Dalian University of Technology) to help widely disseminate the methods and thinking throughout China.
The “query on author” functionality is more precisely described in the presentation of the GraphL-3D algorithm in Sect. ″Community detection and community-topic 3D-graphs approach″.
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Acknowledgements
We wish to thank Prof. Chen Yue who took on the role of main domain expert for this paper. We are also grateful to her for all her advice which helped us improve our critical view of our results. We also wish to warmly thank Richard Dickinson of the Inist in Nancy for the careful proofreading of our paper.
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Lamirel, JC., Gueddari, Y., Wang, Y. et al. Analysis of the dynamics and influence of the research work of Prof. Liu Zeyuan in China featuring a new hybrid approach combining community detection with topic tracking. Scientometrics 126, 6273–6300 (2021). https://doi.org/10.1007/s11192-021-04010-0
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DOI: https://doi.org/10.1007/s11192-021-04010-0