Literature DB >> 33571223

Can we 'feel' the temperature of knowledge? Modelling scientific popularity dynamics via thermodynamics.

Luoyi Fu1, Dongrui Lu1, Qi Li1, Xinbing Wang1, Chenghu Zhou2.   

Abstract

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.

Entities:  

Year:  2021        PMID: 33571223      PMCID: PMC7877646          DOI: 10.1371/journal.pone.0244618

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  18 in total

Review 1.  Network thermodynamics and complexity: a transition to relational systems theory.

Authors:  D C Mikulecky
Journal:  Comput Chem       Date:  2001-07

2.  Let's make science metrics more scientific.

Authors:  Julia Lane
Journal:  Nature       Date:  2010-03-25       Impact factor: 49.962

3.  An index to quantify an individual's scientific research output.

Authors:  J E Hirsch
Journal:  Proc Natl Acad Sci U S A       Date:  2005-11-07       Impact factor: 11.205

4.  At the root of the wood wide web: self recognition and non-self incompatibility in mycorrhizal networks.

Authors:  Manuela Giovannetti; Luciano Avio; Paola Fortuna; Elisa Pellegrino; Cristiana Sbrana; Patrizia Strani
Journal:  Plant Signal Behav       Date:  2006-01

5.  Research funding. Measuring the results of science investments.

Authors:  Julia Lane; Stefano Bertuzzi
Journal:  Science       Date:  2011-02-11       Impact factor: 47.728

6.  Approximate von Neumann entropy for directed graphs.

Authors:  Cheng Ye; Richard C Wilson; César H Comin; Luciano da F Costa; Edwin R Hancock
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2014-05-12

7.  Interdisciplinary research has consistently lower funding success.

Authors:  Lindell Bromham; Russell Dinnage; Xia Hua
Journal:  Nature       Date:  2016-06-30       Impact factor: 49.962

8.  Thermodynamic Analysis of Time Evolving Networks.

Authors:  Cheng Ye; Richard C Wilson; Luca Rossi; Andrea Torsello; Edwin R Hancock
Journal:  Entropy (Basel)       Date:  2018-10-02       Impact factor: 2.524

9.  Thermodynamics and signatures of criticality in a network of neurons.

Authors:  Gašper Tkačik; Thierry Mora; Olivier Marre; Dario Amodei; Stephanie E Palmer; Michael J Berry; William Bialek
Journal:  Proc Natl Acad Sci U S A       Date:  2015-09-01       Impact factor: 11.205

10.  The possible role of resource requirements and academic career-choice risk on gender differences in publication rate and impact.

Authors:  Jordi Duch; Xiao Han T Zeng; Marta Sales-Pardo; Filippo Radicchi; Shayna Otis; Teresa K Woodruff; Luís A Nunes Amaral
Journal:  PLoS One       Date:  2012-12-12       Impact factor: 3.240

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