Understanding scientific knowledge evolution patterns based on egocentric network perspective

Scientific knowledge evolution is an important signal for the innovative development of science and technology. As we know, new concepts and ideas are frequently born out of extensive recombination of existing concepts or notions. The evolution of a single knowledge unit or concept can be transformed into the formation of its ego-centered network from the perspective of combination innovation. Specifically, we proposed the eight research hypotheses from three aspects, namely, preferential attachment, transitivity, and homophily mechanisms. The 10,462 egocentric networks of scientific knowledge were extracted from knowledge co-occurrence network (KCN), and the Exponential Random Graph Models (ERGMs) were applied to model these sample networks individually, taking into account the influence of endogenous network structure and exogenous knowledge attribute variables. By conducting large-scale analytics on the fitting results, we found that (1) the degree centrality has a positive effect on knowledge evolution in the 99.9% sample networks, while the clustering coefficient contributes to the knowledge evolution in 56.8% sample networks at the 0.05 significance level; (2) the adoption behavior and domain impact of authors positively influence the scientific knowledge evolution, respectively, in the 93.5% and 80.8% sample networks; and (3) the knowledge type as well as the journal rank has an impact on the knowledge network evolution, demonstrating the homophily mechanism during the evolution of scientific knowledge.