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

arXiv: https://arxiv.org/abs/2603.17594 · HTML: https://mlsanity.com/paper/2603.17594/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 45 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change](https://mlsanity.com/paper/1606.02821/) (2016)
- [The network structure of scientific revolutions](https://mlsanity.com/paper/2010.08381/) (2020)
- [Conceptual structure and the growth of scientific knowledge](https://mlsanity.com/paper/2204.09747/) (2022)

## Abstract (arXiv)

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.