Scientists' bounded mobility on the epistemic landscape

Despite persistent efforts in revealing the temporal patterns in scientific careers, little attention has been paid to the spatial patterns of scientific activities in the knowledge space. Here, we consider scientists' publication sequence as walks on the quantifiable epistemic landscape constructed by the manifold learning algorithm based on the semantic proximity among papers learned by the document-embedding method or the relational proximity learned by the graph-embedding method, aiming to reveal the individual research topic dynamics and association between research radius with academic performance. Intuitively, the visualization shows the localized and bounded nature of mobile trajectories. We further find that distributions of scientists' transition radius and transition pace are both left-skewed compared with the results of controlled experiments. Then, we observe the mixed exploration and exploitation pattern and the corresponding strategic trade-off in the research transition, where scientists both deepen their previous research with frequency bias and explore new research with knowledge proximity bias. We further develop a bounded exploration-exploitation model to reproduce the patterns. Moreover, the association between scientists' research radius and academic performance shows that extensive exploration will not lead to a sustained increase in academic output but a decrease in impact. In addition, we also note that disruptive findings are more derived from an extensive transition, whereas there is a saturation in this association. Our findings are instrumental for designing the course of an academic career and enlightening the investment of scientific practicing resources.