Multidimensional Analysis of Linguistic Networks

  • Tanya AraújoEmail author
  • Sven Banisch
Part of the Understanding Complex Systems book series (UCS)


Network-based approaches play an increasingly important role in the analysis of data even in systems in which a network representation is not immediately apparent. This is particularly true for linguistic networks, which use to be induced from a linguistic data set for which a network perspective is only one out of several options for representation. Here we introduce a multidimensional framework for network construction and analysis with special focus on linguistic networks. Such a framework is used to show that the higher is the abstraction level of network induction, the harder is the interpretation of the topological indicators used in network analysis. Several examples are provided allowing for the comparison of different linguistic networks as well as to networks in other fields of application of network theory. The computation and the intelligibility of some statistical indicators frequently used in linguistic networks are discussed. It suggests that the field of linguistic networks, by applying statistical tools inspired by network studies in other domains, may, in its current state, have only a limited contribution to the development of linguistic theory.


Network Analysis Network Representation Stylize Fact Preferential Attachment Abstraction Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [Acemoglu et al.(2013)Acemoglu, Ozdaglar, and Tahbaz-Salehi]
    Acemoglu, D., Ozdaglar, A., Tahbaz-Salehi, A.: Systemic risk and stability in financial networks. Tech. rep. National Bureau of Economic Research (2013)Google Scholar
  2. [Albert et al.(1999)Albert, Jeong, and Barabási]
    Albert, R., Jeong, H., Barabási, A.-L.: Internet: Diameter of the world-wide web. Nature 401(6749), 130–131 (1999)CrossRefGoogle Scholar
  3. [Arbesman et al.(2010)Arbesman, Strogatz, and Vitevitch]
    Arbesman, S., Strogatz, S.H., Vitevitch, M.S.: Comparative Analysis of Networks of Phonologically Similar Words in English and Spanish. Entropy 12(3), 327–337 (2010), doi:10.3390/e12030327CrossRefGoogle Scholar
  4. [Bassett et al.(2009)Bassett, Bullmore, Meyer-Lindenberg, Apud, Weinberger, and Coppola]
    Bassett, D.S., Bullmore, E.T., Meyer-Lindenberg, A., Apud, J.A., Weinberger, D.R., Coppola, R.: Cognitive fitness of cost-efficient brain functional networks. Proceedings of the National Academy of Sciences 106(28), 11747–11752 (2009)CrossRefGoogle Scholar
  5. [Bassett and Gazzaniga(2011)]
    Bassett, D.S., Gazzaniga, M.S.: Understanding complexity in the human brain. Trends in Cognitive Sciences 15(5), 200–209 (2011), doi:10.1016/j.tics.2011.03.006CrossRefGoogle Scholar
  6. [Battiston et al.(2010)Battiston, Glattfelder, Garlaschelli, Lillo, and Caldarelli]
    Battiston, S., Glattfelder, J.B., Garlaschelli, D., Lillo, F., Caldarelli, G.: The structure of financial networks. In: Network Science, pp. 131–163. Springer (2010)Google Scholar
  7. [Blanchard et al.(2011)Blanchard, Petroni, Serva, and Volchenkov]
    Blanchard, P., Petroni, F., Serva, M., Volchenkov, D.: Geometric representations of language taxonomies. Computer Speech & Language 25(3), 679–699 (2011), doi:10.1016/j.csl.2010.05.003CrossRefGoogle Scholar
  8. [Borgatti(2005)]
    Borgatti, S.P.: Centrality and network flow. Social Networks 27(1), 55–71 (2005), doi:10.1016/j.socnet.2004.11.008CrossRefGoogle Scholar
  9. [Borgatti and Everett(2006)]
    Borgatti, S.P., Everett, M.G.: A Graph-theoretic perspective on centrality. Social Networks 28(4), 466–484 (2006), doi:
  10. [Borge-Holthoefer and Arenas(2010)]
    Borge-Holthoefer, J., Arenas, A.: Categorizing words through semantic memory navigation. The European Physical Journal B 74(2), 265–270 (2010) (English), doi:10.1140/epjb/e2010-00058-9Google Scholar
  11. [Boss et al.(2004)Boss, Elsinger, Summer, and Thurner]
    Boss, M., Elsinger, H., Summer, M., Thurner, S.: An empirical analysis of the network structure of the Austrian interbank market. Oesterreichesche Nationalbank Financial stability Report 7, 77–87 (2004)Google Scholar
  12. [Broder et al.(2000)Broder, Kumar, Maghoul, Raghavan, Rajagopalan, Stata, Tomkins, and Wiener]
    Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., Wiener, J.: Graph structure in the web. Computer Networks 33(1), 309–320 (2000)CrossRefGoogle Scholar
  13. [Choudhury et al.(2010)Choudhury, Ganguly, Maiti, Mukherjee, Brusch, Deutsch, and Peruani]
    Choudhury, M., Ganguly, N., Maiti, A., Mukherjee, A., Brusch, L., Deutsch, A., Peruani, F.: Modeling discrete combinatorial systems as alphabetic bipartite networks: Theory and applications. Phys. Rev. E 81(3), 036103 (2010), doi:10.1103/PhysRevE.81.036103Google Scholar
  14. [Colizza et al.(2007)Colizza, Pastor-Satorras, and Vespignani]
    Colizza, V., Pastor-Satorras, R., Vespignani, A.: Reaction-diffusion processes and metapopulation models in heterogeneous networks. Nature Physics 3(4), 276–282 (2007)CrossRefGoogle Scholar
  15. [Ferrer i Cancho(2012)]
    Ferrer i Cancho, R.: Bibliography on linguistic, cognitive and brain networks (2012),
  16. [Ferrer i Cancho et al.(2007)Ferrer i Cancho, Mehler, Pustylnikov, and Díaz-Guilera]
    Ferrer i Cancho, R., Mehler, A., Pustylnikov, O., Díaz-Guilera, A.: Correlations in the organization of large-scale syntactic dependency networks. In: TextGraphs-2: Graph-Based Algorithms for Natural Language Processing, pp. 65–72 (2007)Google Scholar
  17. [Ferrer i Cancho et al.(2004)Ferrer i Cancho, Solé, and Köhler]
    Ferrer i Cancho, R., Solé, R.V., Köhler, R.: Patterns in syntactic dependency networks. Phys. Rev. E 69(5), 051915 (2004), doi:10.1103/PhysRevE.69.051915Google Scholar
  18. [Fortunato and Barthelemy(2007)]
    Fortunato, S., Barthelemy, M.: Resolution limit in community detection. Proceedings of the National Academy of Sciences 104(1), 36–41 (2007)CrossRefGoogle Scholar
  19. [Goñi et al.(2011)Goñi, Arrondo, Sepulcre, Martincorena, Vélez de Mendizábal, Corominas-Murtra, Bejarano, Ardanza-Trevijano, Peraita, Wall, and Villoslada]
    Goñi, J., Arrondo, G., Sepulcre, J., Martincorena, I., de Mendizábal, N.V., Corominas-Murtra, B., Bejarano, B., Ardanza-Trevijano, S., Peraita, H., Wall, D.P., Villoslada, P.: The semantic organization of the animal category: evidence from semantic verbal fluency and network theory. Cognitive Processing 12(2), 183–196 (2011) (English), doi:10.1007/s10339-010-0372-xGoogle Scholar
  20. [Grabska-Gradzinska et al.(2012)Grabska-Gradzinska, Kulig, Kwapien, and S.]
    Grabska-Gradzinska, I., Kulig, A., Kwapien, J., Drozdz, S.: Complex network analysis of literary and scientific texts. International Journal of Modern Physics C 23(07), 1250051 (2012), doi:10.1142/S0129183112500519CrossRefGoogle Scholar
  21. [Gravino et al.(2012)Gravino, Servedio, Barrat, and Loreto]
    Gravino, P., Servedio, V.D.P., Barrat, A., Loreto, V.: Complex Structures and Semantics in Free Word Association. Advances in Complex Systems 15(03n04), 1250054 (2012), doi:10.1142/S0219525912500543Google Scholar
  22. [He et al.(2012)He, Sui, Yu, Turner, Ho, Sponheim, Manoach, Clark, and Calhoun]
    He, H., Sui, J., Yu, Q., Turner, J.A., Ho, B.-C., Sponheim, S.R., Manoach, D.S., Clark, V.P., Calhoun, V.D.: Altered small-world brain networks in schizophrenia patients during working memory performance. PloS One 7(6), e38195 (2012)Google Scholar
  23. [Huizinga and Nicodème(2004)]
    Huizinga, H., Nicodème, G.: Are international deposits tax-driven. Journal of Public Economics 88(6), 1093–1118 (2004)CrossRefGoogle Scholar
  24. [Iyengar et al.(2012)Iyengar, Veni Madhavan, Zweig, and Natarajan]
    Iyengar, S.R., Veni Madhavan, C.E., Zweig, K.A., Natarajan, A.: Understanding Human Navigation Using Network Analysis. Topics in Cognitive Science 4(1), 121–134 (2012), doi:10.1111/j.1756-8765.2011.01178.xCrossRefGoogle Scholar
  25. [Kaldor(1956)]
    Kaldor, N.: A model of economic growth. The Economic Journal 67(268), 591–624 (1956)CrossRefGoogle Scholar
  26. [Konect(2014)]
    Konect: OpenFlights network dataset and US airports network dataset – KONECT (2014)Google Scholar
  27. [Landauer and Dutnais(1997)]
    Landauer, T.K., Dutnais, S.T.: A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 211–240 (1997)Google Scholar
  28. [Lerner et al.(2009)Lerner, Ogrocki, and Thomas]
    Lerner, A., Ogrocki, P.K., Thomas, P.J.: Network Graph Analysis of Category Fluency Testing. Cognitive & Behavioral Neurology 22(1), 45–52 (2009)CrossRefGoogle Scholar
  29. [Li and Cai(2004)]
    Li, W., Cai, X.: Statistical analysis of airport network of China. Physical Review E 69(4), 046106 (2004)Google Scholar
  30. [Mantegna(1999)]
    Mantegna, R.N.: Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems 11(1), 193–197 (1999)CrossRefGoogle Scholar
  31. [McGuire and Tarashev(2006)]
    McGuire, P., Tarashev, N.: Tracking international bank flows. BIS Quarterly Review, 27–40 (2006)Google Scholar
  32. [Mehler et al.(2007)Mehler, Geibel, and Pustylnikov]
    Mehler, A., Geibel, P., Pustylnikov, O.: Structural classifiers of text types: Towards a novel model of text representation. LDV Forum: Zeitschrift für Computerlinguistik und Sprachtechnologie; GLDV-Journal for Computational Linguistics and Language Technology 22(2), 51–66 (2007)Google Scholar
  33. [Menon(2011)]
    Menon, V.: Large-scale brain networks and psychopathology: a unifying triple network model. Trends in Cognitive Sciences 15(10), 483–506 (2011)CrossRefGoogle Scholar
  34. [Meusel et al.(2014)Meusel, Vigna, Lehmberg, and Bizer]
    Meusel, R., Vigna, S., Lehmberg, O., Bizer, C.: Graph Structure in the Web – Revisited. Accepted paper at the 23rd International World Wide Web Conference (WWW 2014), Web Science Track, Seoul, Korea (April 2014)Google Scholar
  35. [Miller et al.(2010)Miller, Bliss, and Wolfe]
    Miller, B.A., Bliss, N.T., Wolfe, P.J.: Toward signal processing theory for graphs and non-Euclidean data. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 5414–5417. IEEE (2010)Google Scholar
  36. [Miller(1995)]
    Miller, G.A.: WordNet: A Lexical Database for English. Communications of the ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  37. [Minoiu and Reyes(2011)]
    Minoiu, C., Reyes, J.A.: Network Analysis of Global Banking: 1978-2009. International Monetary Fund (2011)Google Scholar
  38. [Mukherjee et al.(2009)Mukherjee, Choudhury, Basu, and Ganguly]
    Mukherjee, A., Choudhury, M., Basu, A., Ganguly, N.: Self-organization of the Sound Inventories: Analysis and Synthesis of the Occurrence and Co-occurrence Networks of Consonants. Journal of Quantitative Linguistics 16(2), 157–184 (2009), doi:10.1080/09296170902734222CrossRefGoogle Scholar
  39. [Nelson et al.(2004)Nelson, McEvoy, and Schreiber]
    Nelson, D.L., McEvoy, C.L., Schreiber, T.A.: The University of South Florida free association, rhyme, and word fragment norms. Behav. Res. Methods. Instrum. Comput. 36(3), 402–407 (2004)CrossRefGoogle Scholar
  40. [Newman et al.(2003)Newman, Barabási, and Watts]
    Newman, M.E.J., Barabási, A.-L., Watts, D.J.: The Structure and Dynamics of Networks. Princeton University Press, Princeton (2003)Google Scholar
  41. [Newman(2004)]
    Newman, M.E.J.: Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems 38(2), 321–330 (2004)CrossRefGoogle Scholar
  42. [Opsahl et al.(2010)Opsahl, Agneessens, and Skvoretz]
    Opsahl, T., Agneessens, F., Skvoretz, J.: Node Centrality in Weighted Networks: Generalizing Degree and Shortest Paths. Social Networks 3(32), 245–251 (2010)CrossRefGoogle Scholar
  43. [Peng and Hengartner(2002)]
    Peng, R.D., Hengartner, N.: Quantitative Analysis of Literary Styles. The American Statistician 56, 2002 (2002)Google Scholar
  44. [Podobnik et al.(2011)Podobnik, Valentinčič, Horvatić, and Stanley]
    Podobnik, B., Valentinčič, A., Horvatić, D., Eugene Stanley, H.: Asymmetric Levy flight in financial ratios. Proceedings of the National Academy of Sciences 108(44), 17883–17888 (2011)CrossRefGoogle Scholar
  45. [Pustylnikov(2007)]
    Pustylnikov, O.: Guessing Text Type by Structure. In: Proceedings of the 12th ESSLLI Student Session (2007)Google Scholar
  46. [Salton et al.(1975)Salton, Wong, and Yang]
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975), doi:10.1145/361219.361220CrossRefGoogle Scholar
  47. [Salton(1989)]
    Salton, G.: Automatic text processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)Google Scholar
  48. [Serva et al.(2011)Serva, Petroni, Volchenkov, and Wichmann]
    Serva, M., Petroni, F., Volchenkov, D., Wichmann, S.: Malagasy Dialects and the Peopling of Madagascar. J. R. Soc. Interface 9(66), 54–67 (2011)CrossRefGoogle Scholar
  49. [Shuman et al.(2013)Shuman, Narang, Frossard, Ortega, and Vandergheynst]
    Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine 30(3), 83–98 (2013)CrossRefGoogle Scholar
  50. [Soares et al.(2005)Soares, Corso, and Lucena]
    Soares, M.M., Corso, G., Lucena, L.S.: The network of syllables in Portuguese. Physica A: Statistical Mechanics and its Applications 355(2-4), 678–684 (2005), doi:10.1016/j.physa.2005.03.017CrossRefGoogle Scholar
  51. [Solé et al.(2010)Solé, Corominas-Murtra, Valverde, and Steels]
    Solé, R.V., Corominas-Murtra, B., Valverde, S., Steels, L.: Language networks: Their structure, function, and evolution. Complexity 15, 20–26 (2010), doi:10.1002/cplx.20305CrossRefGoogle Scholar
  52. [Soramäki et al.(2007)Soramäki, Bech, Arnold, Glass, and Beyeler]
    Soramäki, K., Bech, M.L., Arnold, J., Glass, R.J., Beyeler, W.E.: The topology of interbank payment flows. Physica A: Statistical Mechanics and its Applications 379(1), 317–333 (2007)CrossRefGoogle Scholar
  53. [Sowa(1991)]
    Sowa, J.F.: Principles of Semantic Networks. Morgan Kaufmann (1991)Google Scholar
  54. [Spelta and Araújo(2012)]
    Spelta, A., Araújo, T.: The topology of cross-border exposures:beyond the minimal spanning tree approach. Physica A: Statistical Mechanics and its Applications 391, 5572–5583 (2012)CrossRefGoogle Scholar
  55. [Sporns et al.(2005)Sporns, Tononi, and Kötter]
    Sporns, O., Tononi, G., Kötter, R.: The Human Connectome: A Structural Description of the Human Brain. PLoS Comput. Biol. 1(4), e42 (2005), doi:10.1371/journal.pcbi.0010042Google Scholar
  56. [Storm(1980)]
    Storm, C.: The Semantic Structure of Animal Terms: A Developmental Study. International Journal of Behavioral Development 3(4), 381–407 (1980), doi:10.1177/016502548000300403CrossRefGoogle Scholar
  57. [Supekar et al.(2008)Supekar, Menon, Rubin, Musen, and Greicius]
    Supekar, K., Menon, V., Rubin, D., Musen, M., Greicius, M.D.: Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Computational Biology 4(6), e1000100 (2008)Google Scholar
  58. [Vilela Mendes et al.(2002)Vilela Mendes, Lima, and Araújo]
    Vilela Mendes, R., Lima, R., Araújo, T.: A process-reconstruction analysis of market fluctuations. International Journal of Theoretical and Applied Finance 5(08), 797–821 (2002)MathSciNetCrossRefGoogle Scholar
  59. [Watts and Strogatz(1998)]
    Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  60. [Yu et al.(2011)Yu, Liu, and Xu]
    Yu, S., Liu, H., Xu, C.: Statistical properties of Chinese phonemic networks. Physica A: Statistical Mechanics and its Applications 390(7), 1370–1380 (2011), doi:10.1016/j.physa.2010.12.019CrossRefGoogle Scholar
  61. [Zamora-López et al.(2011)Zamora-López, Russo, Gleiser, Zhou, and Kurths]
    Zamora-López, G., Russo, E., Gleiser, P.M., Zhou, C., Kurths, J.: Characterizing the complexity of brain and mind networks. Philosophical Transactions of the Royal Society A 369(1952), 3730–3747 (2011)CrossRefGoogle Scholar
  62. [Zhao et al.(2012)Zhao, Liu, Wang, Liu, Xi, Guo, Jiang, Jiang, and Wang]
    Zhao, X., Liu, Y., Wang, X., Liu, B., Xi, Q., Guo, Q., Jiang, H., Jiang, T., Wang, P.: Disrupted small-world brain networks in moderate Alzheimer’s disease: a resting-state FMRI study. PloS One 7(3), e33540 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.ISEG (Lisboa School of Economics and Management) of the University of Lisbon and, Research Unit on Complexity in Economics (UECE)LisboaPortugal
  2. 2.Max Planck Institute for Mathematics in the SciencesLeipzigGermany

Personalised recommendations