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The complex networks approach for authorship attribution of books

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  • Mehri, Ali
  • Darooneh, Amir H.
  • Shariati, Ashrafalsadat

Abstract

Authorship analysis by means of textual features is an important task in linguistic studies. We employ complex networks theory to tackle this disputed problem. In this work, we focus on some measurable quantities of word co-occurrence network of each book for authorship characterization. Based on the network features, attribution probability is defined for authorship identification. Furthermore, two scaling exponents, q-parameter and α-exponent, are combined to classify personal writing style with acceptable high resolution power. The q-parameter, generally known as the nonextensivity measure, is calculated for degree distribution and the α-exponent comes from a power law relationship between number of links and number of nodes in the co-occurrence network constructed for different books written by each author. The applicability of the presented method is evaluated in an experiment with thirty six books of five Persian litterateurs. Our results show high accuracy rate in authorship attribution.

Suggested Citation

  • Mehri, Ali & Darooneh, Amir H. & Shariati, Ashrafalsadat, 2012. "The complex networks approach for authorship attribution of books," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2429-2437.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:7:p:2429-2437
    DOI: 10.1016/j.physa.2011.12.011
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    References listed on IDEAS

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    1. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, April.
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    Cited by:

    1. Mehri, Ali & Jamaati, Maryam, 2021. "Statistical metrics for languages classification: A case study of the Bible translations," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    2. Jennifer A. Byrne & Cyril Labbé, 2017. "Striking similarities between publications from China describing single gene knockdown experiments in human cancer cell lines," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1471-1493, March.
    3. de Arruda, Henrique F. & Marinho, Vanessa Q. & Lima, Thales S. & Amancio, Diego R. & Costa, Luciano da F., 2018. "An image analysis approach to text analytics based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 110-120.
    4. Zhao, Na & Li, Jie & Wang, Jian & Li, Tong & Yu, Yong & Zhou, Tao, 2020. "Identifying significant edges via neighborhood information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    5. Quispe, Laura V.C. & Tohalino, Jorge A.V. & Amancio, Diego R., 2021. "Using virtual edges to improve the discriminability of co-occurrence text networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    6. Tohalino, Jorge V. & Amancio, Diego R., 2018. "Extractive multi-document summarization using multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 526-539.
    7. Akimushkin, Camilo & Amancio, Diego R. & Oliveira, Osvaldo N., 2018. "On the role of words in the network structure of texts: Application to authorship attribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 49-58.

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