Yanbo Zhang and Lingfei Wu
Like the fashion industry, sciences also experience the surge and decay of areas. From the perspective of attention dynamics, this is the natural consequence of the flow of scientists’ collective attention of across different knowledge domains. Different from previous attempts that map the landscape of sciences directly by the topology of knowledge networks (in which nodes are papers or journals and edges are citations or reader-clickstreams), we used the singular vector decomposition (SVD) method to project the analyzed citation networks into a low-dimension Euclidean space. We find that the diffusion of attention in this pace explains 1) the super-linear, power-law growth of the number of citations against the number of papers; and 2) the exponential decay of the citing probability over time. We also show that this low-dimension Euclidean space projection of knowledge map allows us to predict the growth trend of different knowledge domains from their current status. To construct the citation networks we used 5 x 10^5 physics papers published in the past century and 1.3 x 10^5 computer science papers published in the past forty years.