Growing Networks – Modelling the Growth of Word Association Networks for Hungarian and English
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Słowa kluczowe

complex networks
semantic networks
word association
growing network model

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Kovács, L., Orosz, K., & Pollner, P. (2021). Growing Networks – Modelling the Growth of Word Association Networks for Hungarian and English . Investigationes Linguisticae, (45), 67–82.


In the new era of information and communication technology, the representation of information is of increasing importance. Knowing how words are connected to each other in the mind and what processes facilitate the creation of connections could result in better optimized applications, e.g. in computer aided education or in search engines.

This paper models the growth process of a word association database with an algorithm. We present the network structure of word associations for an agglutinative language and compare it with the network of English word associations. Using the real-world data so obtained, we create a model that reproduces the main features of the observed growth process and show the evolution of the network. The model describes the growth of the word association data as a mixture of a topic based process and a random process.

The model makes it possible to gain insight into the overall processes which are responsible for creating an interconnected mental lexicon.
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Barabási, A.-L. 2016: Network Science. Cambridge: Cambridge University Press.

Barabási, A.-L. and Albert, R. 1999: Emergence of scaling in random networks. Science 286(5439): 509–512.

Barrat, A., M. Barthlemy and Vespignani, A. 2008: Dynamical Processes on Complex Networks. Cambridge: Cambridge University Press.

Barthélemy, M. 2011: Spatial networks. Physics Reports 499(1): 1–101.

Biemann, C. 2006: Chinese whispers: an efficient graph clustering algorithm and its application to natural language processing problems. In: Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing. New York: Association for Computational Linguistics. 73–80.

Bybee, J. 1995: Regular morphology and the lexicon. Language and Cognitive Processes 10(5): 425–455.

Cattuto, C., Loreto, V. and Pietronero, L. 2007: Semiotic dynamics and collaborative tagging. Proceedings of the National Academy of Sciences 104(5): 1461–1464.

Collins, A. M. and Loftus, E. F. 1975: A Spreading-Activation Theory of Semantic Processing". Psychological Review 82(6): 407–428.

Cramer, P. 1968: Word Association. New York: Academic Press.

De Deyne, S. and Storms, G. 2008a: Word Associations: Norms for 1,424 Dutch words in a continous task. Behavior Research Methods 40(1): 198–205.

De Deyne, S. and Storms, G. 2008b: Word associations: Network and semantic properties. Behavior Research Methods 40(1): 213–231.

De Deyne, S., Verheyen, S. and Storms, G. 2016: Structure and Organization of the Mental Lexicon: A Network Approach Derived from Syntactic Dependency Relations and Word Associations. In: Mehler, A. et al. (eds.) Towards a Theoretical Framework for Analyzing Complex Linguistic Networks. Berlin–Heidelberg: Springer. 47–79.

Dorogovtsev, S. N., and Mendes, J. F. F. 2001: Language as an evolving word web. Proceedings of the Royal Society of London. Series B: Biological Sciences 268(1485): 2603–2606.

Easley, D. and Kleinberg, J. 2010: Networks, Crowds and Markets. Reasoning about a Highly Connected World. Cambridge: Cambridge University Press.

É. Kiss, K. 2002: The Syntax of Hungarian. Cambridge: Cambridge University Press.

Fellbaum, C. (ed.) 1998: WordNet: An Electronic Lexical Database. Cambridge: MIT Press.

Ferrer i Cancho, R. and Solé, R. V. 2001: The small world of human language, Proceedings of the Royal Society of London. Series B: Biological Sciences 268(1482): 2261–2265.

Figueroa, G. J., González, E. G. and Solís, V. M. 1976: An approach to the problem of meaning: Semantic networks. Journal of Psycholinguistic Research 5(2): 107–115.

Gravino, P. et al. 2012: Complex structures and semantics in free word association. Advances in Complex Systems 15(3-4): 1250054.

Hébert-Dufresne, L. et al. 2011: Structural preferential attachment: Network organization beyond the link. Physical Review Letters 107(15): 158702.

Jung, J., Na, L. and Akama, H. 2010: Network Analysis of Korean Word Associations. In: Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics. Los Angeles: Association for Computational Linguistics. 27–35.

Kent, G. H., and Rosanoff, A. J. 1910. A study of association in insanity. American Journal of Insanity 67(1-2): 37–96; 317–390.

Kiss, G. R. et al. 1973: An associative thesaurus of English and its computer analysis. In: Aitken, A. J., Bailey R. W. and Hamilton-Smith, N. (eds.) The Computer and Literary Studies. Edinburgh: Edinburgh University Press. 153–165.

Kovács, L. 2013: Fogalmi rendszerek és lexikai hálózatok a mentális lexikonban. Budapest. Tinta.

Kovács L., Orosz K. and Pollner P. 2021: Networks in the Mental Lexicon – Contributions from Hungarian. Glottotheory 12(2) (to be appear)

Li, J. et al. 2012: Chinese lexical networks: The structure, function and formation. Physica A: Statistical Mechanics and its Applications 391(21): 5254–5263.

Masucci, A. P. and Rodgers, G. J. 2006: Network properties of written human language. Physical Review E 74(2): 026102.

Mehler, A. et al. (eds.) 2016: Towards a Theoretical Framework for Analyzing Complex Linguistic Networks. Berlin–Heidelberg: Springer.

Menczer, F., Fortunato, S. and Davis, C. A. 2020: A First Course in Network Science. Cambridge: Cambridge University Press.

Miller, G. A. 1995: WordNet: a lexical database for English. Communications of the ACM 38 (11): 39–41.

Motter, A. E. et al. 2002: Topology of the conceptual network of language. Physical Review E 65(6): 065102.

Nelson, D. L., McEvoy, C. L. and Schreiber, T. A. 1998: The University of South Florida word association, rhyme, and word fragment norms. Retrieved from

Palla, G. et al. 2005: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435: 814–818.

Postman, L. and Keppel, G. (eds.) 1970: Norms of Word Association. New York: Academic Press.

Quayle, A. P., Siddiqui, A. S. and Jones, S. J. M. 2006: Modeling network growth with assortative mixing. The European Physical Journal B-Condensed Matter and Complex Systems 50(4): 617–630.

Quillian, M. R. 1968: Semantic memory. In: Minsky, M. (ed.) Semantic Information Processing. Cambridge: MIT Press. 227–270.

Rounds, C. H. 2001: Hungarian: An Essential Grammar. London: Routledge.

Rogers, T. T.–McClelland, J. L. 2004: Semantic Cognition. Cambridge: MIT Press.

Ruppenhofer, J. et al. 2016: FrameNet II: Extended Theory and Practice. Available online at [Accessed 31. January 2022.]

Siew, C. S. Q. et al. 2019: Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics. Complexity 2019(5915): 1–24.

Siew, C. S. Q. and Vitevitch, M. S. 2019: The phonographic language network: Using network science to investigate the phonological and orthographic similarity structure of language. Journal of Experimental Psychology General 148(3): 475–500.

Stella, M. 2019: Modelling Early Word Acquisition through Multiplex Lexical Networks and Machine Learning. Big Data and Cognitive Computing 3(1): 10.

Stella, M., Beckage, N. M. and Brede, M. 2017: Multiplex lexical networks reveal patterns in early word acquisition in children. Scientific Reports 7: 46730.

Steyvers, M., and Tenenbaum J. B. 2010: The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth. Cognitive science 29(1): 41–78.

Tibély, G. et al. 2012: Ontologies and tag-statistics. New Journal of Physics 14(5): 053009.

Vitevitch, M. S. et al. 2014: Using complex networks to understand the mental lexicon. Yearbook of the Poznan Linguistic Meeting 1(1): 119–138.

Vitevitch, M. S. 2020: Introduction. In: Vitevitch, M. S. (ed.) Network Science in Cognitive Psychology. New York – London: Routledge. 1–9.