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Predicting the dynamics of scientific activities: A diffusion‐based network analytic methodology
Author(s) -
Zhang Yi,
Wang Ximeng,
Zhang Guangquan,
Lu Jie
Publication year - 2018
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.2018.14505501065
Subject(s) - bibliometrics , data science , field (mathematics) , computer science , scientometrics , management science , data mining , world wide web , mathematics , engineering , pure mathematics
With the rapid explosion of information and the dramatic development of bibliometric techniques in the past decades, it becomes a challenge to comprehensively, extensively, and efficiently understand science maps. Aim‐ing to explore in‐depth insights from science maps and predict the dynamics of scientific activities, this paper, based on the co‐occurrence statistics of terms derived from scientific documents, proposes a diffusion‐based network analytic methodology to conduct the prediction study from two aspects: the research interest of scien‐tific researchers and the evolutionary directions of scientific topics. A case study on academic articles down‐loaded from three leading journals in the field of bibliometrics demonstrates the feasibility of the methodology. The future directions of bibliometrics are identified, such as the application of information technologies to tradi‐tional bibliometric data, the interactions between bibliometrics and science, technology, and innovation policy issues, and individual‐level bibliometrics. The results also provide recommendations as potential research inter‐ests for a set of experts. The proposed method could be a toolkit to conduct forecasting studies for a given technological area or a given discipline, and a recommender system to assist academic researchers in identify‐ing potential research interests and extended areas.