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Comparison of co-occurrence networks of the Chinese and English languages

Author

Listed:
  • Liang, Wei
  • Shi, Yuming
  • Tse, Chi K.
  • Liu, Jing
  • Wang, Yanli
  • Cui, Xunqiang

Abstract

Co-occurrence networks of Chinese characters and words, and of English words, are constructed from collections of Chinese and English articles, respectively. Four types of collections are considered, namely, essays, novels, popular science articles, and news reports. Statistical parameters of the networks are studied, including diameter, average degree, degree distribution, clustering coefficient, average shortest path length, as well as the number of connected subnetworks. It is found that the character and word networks of each type of article in the Chinese language, and the word network of each type of article in the English language all exhibit scale-free and small-world features. The statistical parameters of these co-occurrence networks are compared within the same language and across the two languages. This study reveals some commonalities and differences between Chinese and English languages, and among the four types of articles in each language from a complex network perspective. In particular, it is shown that expressions in English are briefer than those in Chinese in a certain sense.

Suggested Citation

  • Liang, Wei & Shi, Yuming & Tse, Chi K. & Liu, Jing & Wang, Yanli & Cui, Xunqiang, 2009. "Comparison of co-occurrence networks of the Chinese and English languages," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(23), pages 4901-4909.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:23:p:4901-4909
    DOI: 10.1016/j.physa.2009.07.047
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Liang, Wei & Chen, Guanrong, 2016. "Spectral analysis of Chinese language: Co-occurrence networks from four literary genres," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 49-56.
    2. Liang, Wei, 2017. "Spectra of English evolving word co-occurrence networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 802-808.
    3. Yunfei Xing & Justin Z. Zhang & Yuming He & Yueqi Li, 2025. "Toward an ecosystem of non-fungible tokens from mapping public opinions on social media," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-24, December.
    4. Liang, Wei & Wang, Yanli & Shi, Yuming & Chen, Guanrong, 2015. "Co-occurrence network analysis of modern Chinese poems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 284-293.
    5. Zhong, Xiang & Liu, Jiajun & Gao, Yong & Wu, Lun, 2017. "Analysis of co-occurrence toponyms in web pages based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 462-475.
    6. Liang, Wei & Wang, Kunpeng, 2019. "Relationships among the statistical parameters in evolving modern Chinese linguistic co-occurrence networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 532-539.
    7. Liang, Wei & Wang, Yanli & Shi, Yuming & Chen, Guanrong, 2015. "Co-occurrence network analysis of Chinese and English poems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 315-323.
    8. Liang, Wei & Shi, Yuming & Huang, Qiuling, 2014. "Modeling the Chinese language as an evolving network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 268-276.
    9. Gao, Yuyang & Liang, Wei & Shi, Yuming & Huang, Qiuling, 2014. "Comparison of directed and weighted co-occurrence networks of six languages," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 579-589.
    10. Garg, Muskan & Kumar, Mukesh, 2018. "The structure of word co-occurrence network for microblogs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 698-720.

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