Modeling Changing Scientific Concepts with Complex Networks: A Case Study on the Chemical Revolution

TL;DR

This paper introduces a complex network framework to model and analyze the evolution of scientific concepts over time, using the Chemical Revolution as a case study, addressing interpretability and bias issues in existing methods.

Contribution

It develops a novel complex network approach to represent and analyze concept trajectories, improving interpretability and robustness in studying scientific change.

Findings

Higher entropy correlates with increased idea diversity.

Topological density indicates greater connectivity effort.

Onomasiological change links to network complexity.

Abstract

While context embeddings produced by LLMs can be used to estimate conceptual change, these representations are often not interpretable nor time-aware. Moreover, bias augmentation in historical data poses a non-trivial risk to researchers in the Digital Humanities. Hence, to model reliable concept trajectories in evolving scholarship, in this work we develop a framework that represents prototypical concepts through complex networks based on topics. Utilizing the Royal Society Corpus, we analyzed two competing theories from the Chemical Revolution (phlogiston vs. oxygen) as a case study to show that onomasiological change is linked to higher entropy and topological density, indicating increased diversity of ideas and connectivity effort.