报告人： 狄增如 教授 （北京师范大学）
报告时间：2019年10月25日 星期五 16:00-17:00
As many high quality scientific publication databases have become increasingly accessible in recent years, researchers realized that the data should be interpreted from the perspective of complex systems with multiple and evolving interactions between components. Using approaches from complex networks and statistical physics, many emergent phenomena have been identified. The main contribution of network analysis is to reveal the hidden rules and patterns in scientific research by building the linkage between different scales and dimensions of the system. We analyze the publication records of individual scientists, aiming to quantify the topic switching dynamics of scientists and its influence. For each scientist, the relations among her publications are characterized via shared references. We find that the co-citing network of the papers of a scientist exhibits a clear community structure where each major community represents a research topic. Our analysis suggests that scientists tend to have a narrow distribution of the number of topics. However, researchers nowadays switch more frequently between topics than those in the early days. We also find that high switching probability in early career (< 12y) is associated with low overall productivity, while it is correlated with high overall productivity in latter career. Interestingly, the average citation per paper, however, is in all career stages negatively correlated with the switching probability. We propose a model with exploitation and exploration mechanisms that can explain the main observed features.
目前担任《系统工程理论与实践》、《系统与控制纵横》杂志副主编, 中国大百科全书第三版《系统科学卷》副主编，Journal of Economic Organization and Behavior, Journal of Systems Science and Complexity等学术杂志编委.