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

Sofía Aguilar-Valdez; Stefania Degaetano-Ortlieb

physics.soc-phMar 18, 2026

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

Authors: Sofía Aguilar-Valdez, Stefania Degaetano-Ortlieb

Organizations: Language Science and Technology Department, Saarland University

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.

Figures & tables

Figure 1: Diachronic prototypical concepts . This schematic illustrates movements in central and peripheral readings (arrows), and the presence of notable terms (ovals). Over time, air went from the core to the periphery while acid did the opposite, and this was accompanied by the removal of calx , phlogiston , and the emergence of oxygen .

Panel of Figure 2 — Figure 2: Topic models evaluation . These results, produced by evaluating models for the 1800s non-cumulative corpus, were consistent across decades and strategies. Since both metrics degrade beyond 6 topics (↑perplexity, ↓coherence), the optimal number is 6.

Panel of Figure 3 — Figure 3: Topic clusters . By comparing both relevant decades ( 1st row=1780s , 2nd row=1800s ) across sampling strategies ( 1st column=cumulative , 2nd column=non-cumulative ), we observe the cumulative sampling shows topic clusters with stable labels over time, contrary to the non-cumulative that provides more fine-grained representations.

Panel of Figure 4 — Figure 4: Topic entropy over time . While in cumulative sampling (left) entropy trajectories of oxygen-related terms increase over time, non-cumulative sampling (right) shows a decline then rise during the development of oxygen theory (cf. 1772-1789 in Thagard (1990) ).

Panel of Figure 5 — Figure A1: Temporal graphs . The colors illustrate the number of communities which went on decline: starting at 5 (1750s) and ending at 3 (1800s).

Panel of Figure 6 — Figure A2: Network metrics . We used five parameters to interpret network stability over time: nodes size, edge density (where $y=1\times 10^{3}$ , e.g., last number of edges reported is >60 000), communities count, modularity and percolation threshold.

Semantic Change Detection

Semantic change detection identifies how meanings, concepts, or scene contents differ across time in text, images, and other data.

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Computational conceptual history of scientific concepts: From early digital methods to LLMs

Michael Zichert, Arno Simons

Distributional Semantics Semantic Change Detection

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This article situates large language models (LLMs) within the longer history of computational approaches to concept analysis in the history, philosophy, and sociology of science (HPSS). We examine what LLMs add to existing methods, how they inherit longstanding problems, and review recent case studies that employ them. In the first part, we reconstruct computational conceptual history before LLMs by bringing together three strands of work: early digital methods in HPSS, distributional approaches from digital history and related research, and lexical semantic change detection. We provide an overview of the main challenges and opportunities, focusing on corpus construction, operationalization and modelling choices, and evaluation and interpretation. In the second part, we turn to the era of LLMs, starting with a short introduction to LLMs before reviewing LLM-based work on lexical semantic change detection and relevant case studies in HPSS. We then revisit the earlier methodological questions, showing how issues of corpus construction, model choice and training data, operationalization trade-offs, and evaluation and interpretation play out in LLM-based workflows.

Abstract

Semantic Change Detection

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