Research on scientific knowledge evolution patterns based on ego-centered fine-granularity citation network

Abstract

A deep comprehension of the development of scientific knowledge is necessary to properly appreciate technological advancement and the process of knowledge innovation. However, fine-grained citation networks are less frequently utilized to characterize the evolution of scientific knowledge. To bridge this gap, we first constructed the fine-grained citation networks based on the PubMed and Web of Science (WOS) databases, then extracted the ego-centered networks of scientific knowledge, and finally employed Exponential Random Graph Models (ERGMs) to analyze the factors influencing the formation of Ego-centered Fine-granularity Citation Networks (EFCNs), taking into account the endogenous network structure and exogenous knowledge attribute variables. The results reveal that both types of variables play pivotal roles in the evolution of scientific knowledge. Furthermore, we found that (1) the in-degree and the out-degree centrality have a positive effect on knowledge evolution, respectively, in the 98.5 % and 99.9 % sample networks, while the clustering coefficient only has a positive effect on the edge formation of the 6.8 % sample network at the 0.05 significance level. (2) The citation behavior and domain impact of authors positively influence the scientific knowledge evolution, respectively, in the 63.2 % and 78.3 % sample networks. (3) There is a tendency to form citation relationships between scientific knowledge units of similar age in 67.1 % of the sample networks. (4) There is a greater possibility of developing a citation relationship between scientific knowledge with the same journal impact rank and knowledge type. Our findings indicate that the evolution of scientific knowledge is influenced not only by the process of scholarly communication but also by self-organizing mechanisms at the fine granularity level.