Independency of Knowledge Diffusion Analyzed by Inverse Citation Networks

Abstract

Abstract With the development of worldwide knowledge-based economy, structures of knowledge diffusion in scientific research have become extremely complex and dynamic. Properly evaluating the knowledge diffusion would encourage authors to pursue high quality researches. Hence, this paper presents a novel metric of independency of knowledge diffusion (IKD) on the published paper v, defined as the ratio of citation counts of v without its references’ to citation counts of v and its references’ minus their commons’. Utilizing the inverse citation network formed by published papers in American Physical Society (APS) from 1997 and 2016, the experimental results show that the distributions of IKD are following power law behaviors and the values of IKD are affected by citation counts and involved cooperative institutions. It is reasonable to assess the performances of knowledge diffusion by the metric of IKD.