Approaching human language with complex networks
Abstract
The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics).
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Small-world and scale-free properties are universal features of human language on a macro scale.
“Consequently, small-world and scale-free properties are seen as universal features of human language on a macro scale (Ferrer i Cancho et al., 2004; Cong and Liu, 2014).”
From Syntactic Networks of Interlanguage Across L2 Modalities and Proficiency Levels (2021) · cites this paper as Cong and Liu, 2014
Complex networks with the small-world property have far greater clustering coefficients compared with those of random networks.
“Complex networks with the small-world property have both a small average path length and far greater clustering coefficients compared with those of random networks (Watts and Strogatz, 1998; Cong and Liu, 2014).”
From Syntactic Networks of Interlanguage Across L2 Modalities and Proficiency Levels (2021) · cites this paper as Cong and Liu, 2014
Syntactic networks of different language types have the same small-world and scale-free properties as other complex networks.
“By analyzing complex networks, researchers have found that the syntactic networks of different language types have the same small-world and scale-free properties as other complex networks.”
From Syntactic Networks of Interlanguage Across L2 Modalities and Proficiency Levels (2021) · cites this paper as Cong and Liu, 2014
Complex networks with the small-world property have a small average path length.
“Complex networks with the small-world property have both a small average path length and far greater clustering coefficients compared with those of random networks (Watts and Strogatz, 1998; Cong and Liu, 2014).”
From Syntactic Networks of Interlanguage Across L2 Modalities and Proficiency Levels (2021) · cites this paper as Cong and Liu, 2014
Cites
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Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · see citing works
Dynamical Processes on Complex Networks
Barrat, A., Barthélemy, M., & Vespignani, A. (2008) · doi:10.1017/cbo9780511791383 · Cited by 2,704 · cited 1× · see citing works
“The few vertices with high degrees usually behave as hubs of their networks [6].”
BackgroundEfficiency and Complexity in Grammars
Hawkins, J. A. (2004) · doi:10.1093/acprof:oso/9780199252695.001.0001 · Cited by 1,743 · see citing works
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CONSTRUCTING A LANGUAGE: A USAGE-BASED THEORY OF LANGUAGE ACQUISITION
Foster-Cohen, S. (2004) · Studies in Second Language Acquisition · doi:10.1017/s0272263104363059 · Cited by 969 · see citing works
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Syntactic Networks of Interlanguage Across L2 Modalities and Proficiency Levels
Hao, Y., Wang, X., Wu, M., & Liu, H. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.643120 · Cited by 8 · see citing works
“Complex networks with the small-world property have both a small average path length and far greater clustering coefficients compared with those of random networks (Watts and Strogatz, 1998; Cong and Liu, 2014).”
BackgroundSelf-citation
- Authors
- Cong, J., & Liu, H.
- Affiliations
- Zhejiang University (CN)
- NingboTech University (CN)
- Corresponding author
- Liu, Haitao
- ORCID
- Venue
- Physics of Life Reviews
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 11
- Issue
- 4
- Pages
- 598-618
- ISSN
- 1571-0645 (ISSN-L)
- OpenAlex
- W1988661732
- Cited by
- 212
- References
- 5
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 12 · 2023: 12 · 2022: 17 · 2021: 25 · 2020: 21 · 2019: 18 · 2018: 16 · 2017: 23 · 2016: 19 · 2015: 17 · 2014: 14
- FWCI
- 1.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Physics of Life Reviews (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Social Science Foundation of China (11&ZD188)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Cultural Studies › Language and cultural evolution
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Keywords
- human language, complex networks, language network, linguistic typology, network topology, syntactic features, quantitative linguistics, network metrics
- MeSH terms
- China, Communication, Humans, Language, Linguistics, Models, Statistical, Comprehension
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