Can We 'Feel' the Temperature of Knowledge? Modelling Scientific Popularity Dynamics via Thermodynamics

Autor: Qi Li, Dongrui Lu, Luoyi Fu, Chenghu Zhou, Xinbing Wang
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
Computer science
Entropy
Astronomical Sciences
Forests
Infographics
Popular Culture
Random Graphs
Citation analysis
Knowledge modelling
Data Management
Multidisciplinary
Ecology
Flourishing
Physics
05 social sciences
Publications
Computer Science - Social and Information Networks
Research Assessment
Celestial Objects
Knowledge Discovery
Terrestrial Environments
Knowledge generation
Knowledge
Research Design
Citation Analysis
Physical Sciences
Medicine
Thermodynamics
050904 information & library sciences
Graphs
Network Analysis
Research Article
Physics - Physics and Society
Computer and Information Sciences
Neural Networks
Science
Analogy
FOS: Physical sciences
Physics and Society (physics.soc-ph)
050905 science studies
Research and Analysis Methods
Ecosystems
Entropy (information theory)
Social and Information Networks (cs.SI)
Data Visualization
Research
Ecology and Environmental Sciences
Biology and Life Sciences
Models
Theoretical
Galaxies
Data science
Popularity
0509 other social sciences
Neuroscience
Zdroj: PLoS ONE
PLoS ONE, Vol 16, Iss 2, p e0244618 (2021)
DOI: 10.48550/arxiv.2007.13270
Popis: Just like everything in nature, scientific topics flourish and perish. While existing literature well captures article’s life-cycle via citation patterns, little is known about how scientific popularity and impact evolves for a specific topic. It would be most intuitive if we could ‘feel’ topic’s activity just as we perceive the weather by temperature. Here, we conceive knowledge temperature to quantify topic overall popularity and impact through citation network dynamics. Knowledge temperature includes 2 parts. One part depicts lasting impact by assessing knowledge accumulation with an analogy between topic evolution and isobaric expansion. The other part gauges temporal changes in knowledge structure, an embodiment of short-term popularity, through the rate of entropy change with internal energy, 2 thermodynamic variables approximated via node degree and edge number. Our analysis of representative topics with size ranging from 1000 to over 30000 articles reveals that the key to flourishing is topics’ ability in accumulating useful information for future knowledge generation. Topics particularly experience temperature surges when their knowledge structure is altered by influential articles. The spike is especially obvious when there appears a single non-trivial novel research focus or merging in topic structure. Overall, knowledge temperature manifests topics’ distinct evolutionary cycles.
Databáze: OpenAIRE
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