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Multidimensional Analysis of Linguistic Networks

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

Abstract

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.

Keywords

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.

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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

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