透视图(图形)
口译(哲学)
期限(时间)
互补性(分子生物学)
范围(计算机科学)
计算机科学
机制(生物学)
功能(生物学)
知识管理
现象
数据科学
过程(计算)
认识论
生物
进化生物学
人工智能
物理
遗传学
操作系统
哲学
程序设计语言
量子力学
作者
Junwan Liu,Xiaofei Guo,Shuo Xu,Yinglu Song,Kaiyue Ding
标识
DOI:10.1016/j.joi.2022.101372
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI