IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v388y2009i12p2561-2570.html
   My bibliography  Save this article

English and Chinese languages as weighted complex networks

Author

Listed:
  • Sheng, Long
  • Li, Chunguang

Abstract

In this paper, we analyze statistical properties of English and Chinese written human language within the framework of weighted complex networks. The two language networks are based on an English novel and a Chinese biography, respectively, and both of the networks are constructed in the same way. By comparing the intensity and density of connections between the two networks, we find that high weight connections in Chinese language networks prevail more than those in English language networks. Furthermore, some of the topological and weighted quantities are compared. The results display some differences in the structural organizations between the two language networks. These observations indicate that the two languages may have different linguistic mechanisms and different combinatorial natures.

Suggested Citation

  • Sheng, Long & Li, Chunguang, 2009. "English and Chinese languages as weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(12), pages 2561-2570.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:12:p:2561-2570
    DOI: 10.1016/j.physa.2009.02.043
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437109001769
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2009.02.043?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhou, Shuigeng & Hu, Guobiao & Zhang, Zhongzhi & Guan, Jihong, 2008. "An empirical study of Chinese language networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 3039-3047.
    2. E. L. Berlow, 1999. "Strong effects of weak interactions in ecological communities," Nature, Nature, vol. 398(6725), pages 330-334, March.
    3. Ramon Ferrer i Cancho & Ricard V. Solé, 2001. "The Small-World of Human Language," Working Papers 01-03-016, Santa Fe Institute.
    4. Liu, Haitao, 2008. "The complexity of Chinese syntactic dependency networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 3048-3058.
    5. Marchiori, Massimo & Latora, Vito, 2000. "Harmony in the small-world," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 285(3), pages 539-546.
    6. Gary A. Polis, 1998. "Stability is woven by complex webs," Nature, Nature, vol. 395(6704), pages 744-745, October.
    7. Li, Jianyu & Zhou, Jie, 2007. "Chinese character structure analysis based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 629-638.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Jianyu & Zhou, Jie & Luo, Xiaoyue & Yang, Zhanxin, 2012. "Chinese lexical networks: The structure, function and formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5254-5263.
    2. 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.
    3. Bian, Tian & Hu, Jiantao & Deng, Yong, 2017. "Identifying influential nodes in complex networks based on AHP," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 422-436.
    4. Huang, He & Yan, Zhijun & Pan, Yaohui, 2014. "Measuring edge importance to improve immunization performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 532-540.
    5. Shuqing Li & Ying Sun & Dagobert Soergel, 2015. "A new method for automatically constructing domain-oriented term taxonomy based on weighted word co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 1023-1042, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Jianyu & Zhou, Jie & Luo, Xiaoyue & Yang, Zhanxin, 2012. "Chinese lexical networks: The structure, function and formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5254-5263.
    2. 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.
    3. Diego R Amancio, 2015. "Probing the Topological Properties of Complex Networks Modeling Short Written Texts," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.
    4. Yu, Shuiyuan & Liu, Haitao & Xu, Chunshan, 2011. "Statistical properties of Chinese phonemic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1370-1380.
    5. Ghosh, Dipak & Chakraborty, Sayantan & Samanta, Shukla, 2019. "Study of translational effect in Tagore’s Gitanjali using Chaos based Multifractal analysis technique," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1343-1354.
    6. Woon Peng Goh & Kang-Kwong Luke & Siew Ann Cheong, 2018. "Functional shortcuts in language co-occurrence networks," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-18, September.
    7. Igor Linkov & Benjamin Trump & Greg Kiker, 2022. "Diversity and inclusiveness are necessary components of resilient international teams," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-5, December.
    8. Mark J. O. Bagley, 2019. "Networks, geography and the survival of the firm," Journal of Evolutionary Economics, Springer, vol. 29(4), pages 1173-1209, September.
    9. Smith, Thomas Bryan, 2021. "Gang crackdowns and offender centrality in a countywide co-offending network: A networked evaluation of Operation Triple Beam," Journal of Criminal Justice, Elsevier, vol. 73(C).
    10. Yang, Yi & Song, Yue, 2023. "Resistance-distance betweenness: Definition, properties, and application in complex linguistic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    11. Tol, Richard S.J., 2023. "Nobel begets Nobel in economics," Journal of Informetrics, Elsevier, vol. 17(4).
    12. Li, Jianyu & Zhou, Jie, 2007. "Chinese character structure analysis based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 629-638.
    13. Wang, Shuran Cindy & Liu, Xueqin & Liu, Yong & Wang, Hongzhu, 2020. "Benthic-pelagic coupling in lake energetic food webs," Ecological Modelling, Elsevier, vol. 417(C).
    14. Xiao, Wenjun & Liu, Yanxia & Chen, Guanrong, 2014. "Characterizing vertex-degree sequences in scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 291-295.
    15. Wang, Junjie & Zhou, Shuigeng & Guan, Jihong, 2011. "Characteristics of real futures trading networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(2), pages 398-409.
    16. Dai, Lingfei & Dai, Meifeng & Huang, Yu & Li, Yin & Shen, Junjie & Chi, Huijia & Su, Weiyi, 2020. "Searching efficiency of multiple walkers on the weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    17. Giacomini, Henrique Corrêa & De Marco, Paulo & Petrere, Miguel, 2009. "Exploring community assembly through an individual-based model for trophic interactions," Ecological Modelling, Elsevier, vol. 220(1), pages 23-39.
    18. Liu, Haitao, 2008. "The complexity of Chinese syntactic dependency networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 3048-3058.
    19. Liu, Yanyan & Li, Keping & Yan, Dongyang & Gu, Shuang, 2022. "A network-based CNN model to identify the hidden information in text data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    20. Raymond, Ben & Hosie, Graham, 2009. "Network-based exploration and visualisation of ecological data," Ecological Modelling, Elsevier, vol. 220(5), pages 673-683.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:388:y:2009:i:12:p:2561-2570. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.