Exploring Word-Adjacency Networks with Multifractal Time Series Analysis Techniques
A novel method of exploring linguistic networks is introduced by mapping word-adjacency networks to time series and applying multifractal analysis techniques. This approach captures the complex structural patterns of language by encoding network properties—such as clustering coefficients and node de...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/4/356 |
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| author | Jakub Dec Michał Dolina Stanisław Drożdż Robert Kluszczyński Jarosław Kwapień Tomasz Stanisz |
| author_facet | Jakub Dec Michał Dolina Stanisław Drożdż Robert Kluszczyński Jarosław Kwapień Tomasz Stanisz |
| author_sort | Jakub Dec |
| collection | DOAJ |
| description | A novel method of exploring linguistic networks is introduced by mapping word-adjacency networks to time series and applying multifractal analysis techniques. This approach captures the complex structural patterns of language by encoding network properties—such as clustering coefficients and node degrees—into temporal sequences. Using Alice’s Adventures in Wonderland by Lewis Carroll as a case study, both traditional word-adjacency networks and extended versions that incorporate punctuation are examined. The results indicate that the time series derived from clustering coefficients, when following the natural reading order, exhibits multifractal characteristics, revealing inherent complexity in textual organization. Statistical validation confirms that observed multifractal properties arise from genuine correlations rather than from spurious effects. Extending this analysis by taking into account punctuation equally with words, however, changes the nature of the global scaling to a more convolved form that is not describable by a uniform multifractal. An analogous analysis based on the node degrees does not show such rich behaviors, however. These findings reveal a new perspective for quantitative linguistics and network science, providing a deeper understanding of the interplay between text structure and complex systems. |
| format | Article |
| id | doaj-art-efe72dbbab6e4c9a8bd82c69f2887f3f |
| institution | DOAJ |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-efe72dbbab6e4c9a8bd82c69f2887f3f2025-08-20T03:13:51ZengMDPI AGEntropy1099-43002025-03-0127435610.3390/e27040356Exploring Word-Adjacency Networks with Multifractal Time Series Analysis TechniquesJakub Dec0Michał Dolina1Stanisław Drożdż2Robert Kluszczyński3Jarosław Kwapień4Tomasz Stanisz5Faculty of Computer Science and Telecommunications, Cracow University of Technology, 31-155 Kraków, PolandFaculty of Computer Science and Telecommunications, Cracow University of Technology, 31-155 Kraków, PolandFaculty of Computer Science and Telecommunications, Cracow University of Technology, 31-155 Kraków, PolandComplex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, 31-342 Kraków, PolandComplex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, 31-342 Kraków, PolandComplex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, 31-342 Kraków, PolandA novel method of exploring linguistic networks is introduced by mapping word-adjacency networks to time series and applying multifractal analysis techniques. This approach captures the complex structural patterns of language by encoding network properties—such as clustering coefficients and node degrees—into temporal sequences. Using Alice’s Adventures in Wonderland by Lewis Carroll as a case study, both traditional word-adjacency networks and extended versions that incorporate punctuation are examined. The results indicate that the time series derived from clustering coefficients, when following the natural reading order, exhibits multifractal characteristics, revealing inherent complexity in textual organization. Statistical validation confirms that observed multifractal properties arise from genuine correlations rather than from spurious effects. Extending this analysis by taking into account punctuation equally with words, however, changes the nature of the global scaling to a more convolved form that is not describable by a uniform multifractal. An analogous analysis based on the node degrees does not show such rich behaviors, however. These findings reveal a new perspective for quantitative linguistics and network science, providing a deeper understanding of the interplay between text structure and complex systems.https://www.mdpi.com/1099-4300/27/4/356quantitative linguisticscomplex networksvertex observablesclustring coefficienttime seriesmultiscale correlations |
| spellingShingle | Jakub Dec Michał Dolina Stanisław Drożdż Robert Kluszczyński Jarosław Kwapień Tomasz Stanisz Exploring Word-Adjacency Networks with Multifractal Time Series Analysis Techniques Entropy quantitative linguistics complex networks vertex observables clustring coefficient time series multiscale correlations |
| title | Exploring Word-Adjacency Networks with Multifractal Time Series Analysis Techniques |
| title_full | Exploring Word-Adjacency Networks with Multifractal Time Series Analysis Techniques |
| title_fullStr | Exploring Word-Adjacency Networks with Multifractal Time Series Analysis Techniques |
| title_full_unstemmed | Exploring Word-Adjacency Networks with Multifractal Time Series Analysis Techniques |
| title_short | Exploring Word-Adjacency Networks with Multifractal Time Series Analysis Techniques |
| title_sort | exploring word adjacency networks with multifractal time series analysis techniques |
| topic | quantitative linguistics complex networks vertex observables clustring coefficient time series multiscale correlations |
| url | https://www.mdpi.com/1099-4300/27/4/356 |
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