A new interpretation of scientific collaboration patterns from the perspective of symbiosis: An investigation for long-term collaboration in publications. Issue 1 (February 2023)

• Record Type: Journal Article
• Authors: Liu, Junwan; Guo, Xiaofei; Xu, Shuo; Song, Yinglu; Ding, Kaiyue
• Is Part Of: Journal of informetrics. Volume 17:Issue 1(2023)
• Publication Date: 2023-02
• Subjects: Scientific collaboration pattern -- Symbiosis -- Symbiosis degree -- Symbiosis period -- Collaboration network; Library statistics -- Periodicals; Information science -- Statistical methods -- Periodicals; Bibliometrics -- Periodicals; Bibliothèques -- Statistiques -- Périodiques; Sciences de l'information -- Méthodes statistiques -- Périodiques; Bibliométrie -- Périodiques; 020.727
• DOI: 10.1016/j.joi.2022.101372
• Languages: English
• ISSNs: 1751-1577

Abstract

Highlights: A novel interpretation of scientific collaboration patterns is proposed from the perspective of symbiosis. Quantitative metrics for the degree of symbiosis and the dynamics of scientific collaboration are taken into account to exploit the structure and evolution of collaboration patterns. Empirical studies indicate that our symbiosis based framework can interpret very well the developmental and evolutionary trajectories of scientific collaboration. Abstract: Based on complementarity in terms of factors such as skill and knowledge, researchers might build long-term partnerships with one another during their scientific careers. It has been shown that such relationships have a significant positive impact on researchers' scientific performance. However, the preferential connection mechanism in collaboration networks actually suggests the unequal positions of participants in the process of scientific collaboration. This study argues that this phenomenon is very similar to the symbiosis function in the natural world. Hence, this work provides a novel interpretation of scientific collaboration patterns from the perspective of symbiosis. In more detail, long-term collaboration relationships are investigated based on the scope of an academician dataset with multiple fields and an economic dataset. With the aid of a quantitative metric for symbiosis degree, six meta-patterns of the short-term evolution of symbiosis degree are proposed. Furthermore, by exploring the evolution of meta-patterns, four scientific collaboration patterns are summarized according to the common characteristics as follows: leading growth, continuous leadership, chasing each other, and standing on equal footing . Extensive experimental results on an academician dataset with multiple fields show that the collaboration network evolution of four collaboration patterns is consistent with our summarized characteristics based on symbiosis. This indicates that our symbiosis-based framework can be used to effectively interpret the developmental and evolutionary trajectories of scientific collaboration.