Complex networks and human language

While perusing the internet on the subject of scale-free and small world networks I came across this article, which deals with human language as opposed to Ltu's focus on formal languages. I hope it is not too far off topic and if so please delete this contribution. My main motivation to publish this article lies in the fact that scale-free and small world features can in my opinion be seen back in programming environments with extensive libraries and systems making extensive use of DSL's.

Complex dynamic networks are ubiquitous in nature and human society. In recent years research in large-scale networks has seen a rapid development in various disciplines, inspired by the discovery of two features shared by many real-world networks: small-world and scale-free. This paper introduces how human languages can be studied in this light, and reports some work available in this area. There are two directions of exploration. One is to study networks existing in the language system. Various lexical networks can be built based on different relationships between words, being semantic or syntactic. Recent studies have shown that these lexical networks exhibit small-world and scale-free features. The findings of these global structures of the mental lexicon, which are not detectable in traditional analyses on semantic networks, trigger more interests in the global structures of the mental lexicon and language in general from a network perspective. The other direction of exploration is to study networks of language users (i.e. social networks of people in the linguistic community), and their role in language evolution. Social networks also show small-world and scale-free features, which cannot be captured by random or regular network models. In the past, computational models of language change and language emergence often assume a population to have a random or regular structure, and there has been little discussion how network structures may affect the dynamics. In the second part of the paper, a series of simulation models of diffusion of linguistic innovation are used to illustrate the importance of choosing realistic conditions of population structure for modeling language change.. Four types of social networks are compared, which exhibit two categories of diffusion dynamics. While the questions about which type of networks are more appropriate for modeling still remains, we give some preliminary suggestions for choosing the type of social networks for modeling.