Repeat collaboration and scientific innovation: evidence from dynamic ego networks of Nobel laureates

This study explores how collaboration dynamics influence scientific innovation in Physics, Chemistry, and Physiology or Medicine by analyzing the evolving ego networks of Nobel laureates. Using data from the Microsoft Academic Graph and an updated Nobel laureate dataset (1900–2024), we quantify collaboration patterns through collaboration length and cumulative coauthorship strength within dynamic ego networks. Innovation is measured via four field- and time-normalized metrics: Citation, Novelty, Disruption, and Interdisciplinarity. By studying Nobel laureates, exemplars of transformative science, this work tests and extends theories of collaboration dynamics in elite scientific networks, offering insights into the mechanisms driving innovation. Our findings reveal that Nobel-winning papers predominantly stem from new collaborations rather than sustained partnerships. Regression analyses show a negative association between repeat collaboration and innovation, with prolonged coauthoring linked to reduced novelty, disruptiveness, and interdisciplinarity, though this effect is less pronounced in Chemistry, suggesting field-specific dynamics. Career age differences between coauthors mediate this relationship, with larger gaps amplifying negative impacts on citation and disruptiveness, except in Chemistry, where the trend diverges. These insights highlight the value of fresh collaborations in driving transformative science in these fields and suggest tailored strategies for fostering innovation.