Network Text Analysis Framework for Mapping Research Trends: A Neural Architecture Search Case Study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00638738" target="_blank" >RIV/67985807:_____/25:00638738 - isvavai.cz</a>

Výsledek na webu

<a href="https://ceur-ws.org/Vol-4092/paper4.pdf" target="_blank" >https://ceur-ws.org/Vol-4092/paper4.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Network Text Analysis Framework for Mapping Research Trends: A Neural Architecture Search Case Study

Popis výsledku v původním jazyce

Understanding the evolution of research trends is critical for navigating rapidly developing scientific literature. Large Language Models (LLMs) offer powerful tools for analysing scientific texts, enabling the extraction of key concepts and the construction of semantic networks. These capabilities can support the study of emerging ideas and research trends through graph-based representations. In this paper, we present a network-based text analysis framework designed to map the evolution of scientific knowledge. Our goal is to extract conceptual structures from research papers and construct graphs that represent both the occurrence of terms and their interrelationships. The integration of temporal information allows us to track the emergence and transformation of research themes. We demonstrate this framework using a case study on Neural Architecture Search (NAS) field, a fast-growing subfield in machine learning focused on the automated design of neural networks. Using data from ArXiv combined with metadata and citation records from OpenAlex, we construct and analyse graphs of keywords and articles. This allows us to reveal the dynamics of the NAS research landscape and highlight methodological trends and conceptual shifts.

Název v anglickém jazyce

Network Text Analysis Framework for Mapping Research Trends: A Neural Architecture Search Case Study

Popis výsledku anglicky

Understanding the evolution of research trends is critical for navigating rapidly developing scientific literature. Large Language Models (LLMs) offer powerful tools for analysing scientific texts, enabling the extraction of key concepts and the construction of semantic networks. These capabilities can support the study of emerging ideas and research trends through graph-based representations. In this paper, we present a network-based text analysis framework designed to map the evolution of scientific knowledge. Our goal is to extract conceptual structures from research papers and construct graphs that represent both the occurrence of terms and their interrelationships. The integration of temporal information allows us to track the emergence and transformation of research themes. We demonstrate this framework using a case study on Neural Architecture Search (NAS) field, a fast-growing subfield in machine learning focused on the automated design of neural networks. Using data from ArXiv combined with metadata and citation records from OpenAlex, we construct and analyse graphs of keywords and articles. This allows us to reveal the dynamics of the NAS research landscape and highlight methodological trends and conceptual shifts.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EH23_025%2F0008711" target="_blank" >EH23_025/0008711: Vědění ve věku nedůvěry</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 25th Conference Information Technologies – Applications and Theory (ITAT 2025)

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

8

Strana od-do

58-68

Název nakladatele

Technical University & CreateSpace Independent Publishing

Místo vydání

Aachen

Místo konání akce

Telgárt

Datum konání akce

26. 9. 2025

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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