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Derivative of a hypergraph as a tool for linguistic pattern analysis

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  • Criado-Alonso, Ángeles
  • Aleja, David
  • Romance, Miguel
  • Criado, Regino

Abstract

The search for linguistic patterns together with stylometry and forensic linguistics has in the theory of complex networks, its structures and its associated mathematical tools essential resources for representing and analyzing texts. In this paper we introduce a new model able to analyze the mesoscopic relationships between sentences, paragraphs, chapters and texts. This model is supported by several mathematical structures such as the hypergraphs or the concept of derivative graph. The methodology raised from this perspective focuses not only in a quantitative index but also in two peculiar mathematical structures named derivative graph and homogeneity graph. These structures are of singular help to both: detecting the style of an author and determining the linguistic level of a text and, eventually, also for detecting similarities and dissimilarities in texts and even plagiarism.

Suggested Citation

  • Criado-Alonso, Ángeles & Aleja, David & Romance, Miguel & Criado, Regino, 2022. "Derivative of a hypergraph as a tool for linguistic pattern analysis," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    • Handle: RePEc:eee:chsofr:v:163:y:2022:i:c:s0960077922007901
    DOI: 10.1016/j.chaos.2022.112604
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    References listed on IDEAS

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    1. T. S. Evans & R. Lambiotte, 2010. "Line graphs of weighted networks for overlapping communities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(2), pages 265-272, September.
    2. Jay Bagga, 2004. "Old and new generalizations of line graphs," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2004, pages 1-13, January.
    3. Martinčić-Ipšić, Sanda & Margan, Domagoj & Meštrović, Ana, 2016. "Multilayer network of language: A unified framework for structural analysis of linguistic subsystems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 117-128.
    4. Eduardo G Altmann & Janet B Pierrehumbert & Adilson E Motter, 2009. "Beyond Word Frequency: Bursts, Lulls, and Scaling in the Temporal Distributions of Words," PLOS ONE, Public Library of Science, vol. 4(11), pages 1-7, November.
    5. Criado-Alonso, Ángeles & Battaner-Moro, Elena & Aleja, David & Romance, Miguel & Criado, Regino, 2021. "Enriched line graph: A new structure for searching language collocations," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    6. Michael Brusco & J Dennis Cradit & Douglas Steinley, 2021. "A comparison of 71 binary similarity coefficients: The effect of base rates," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-19, April.
    7. Aleja, David & Criado, Regino & García del Amo, Alejandro J. & Pérez, Ángel & Romance, Miguel, 2019. "Non-backtracking PageRank: From the classic model to hashimoto matrices," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 283-291.
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    Cited by:

    1. Amiri, Babak & Karimianghadim, Ramin, 2024. "A novel text clustering model based on topic modelling and social network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    2. Contreras-Aso, Gonzalo & Criado, Regino & Vera de Salas, Guillermo & Yang, Jinling, 2023. "Detecting communities in higher-order networks by using their derivative graphs," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).

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