Community-centric modeling of citation dynamics explains collective citation patterns across different knowledge systems

Human knowledge systems, including science, law, and invention, are built on publications and the citations that link them. Statistical analysis of scientific citations has revealed patterns that illuminate the mechanisms driving knowledge production, recognition, and formalization. Yet most quantitative findings remain confined to science, leaving open the question of whether these dynamics are common features of how knowledge evolves. Here we show, using a corpus of U.S. case law alongside scientific and patent citations, that law, science, and patents share similar statistical citation dynamics. We propose a holistic model capturing three core mechanisms that drive collective dynamics, replicate observed citation patterns including previously unexplained phenomena like delayed recognition, and better predict future impact. Our findings suggest that common mechanisms govern large-scale patterns across diverse knowledge systems, offering generalizable perspectives on discovery and innovation.