Article

DIFFERENCES BETWEEN NORMAL AND SHUFFLED TEXTS: STRUCTURAL PROPERTIES OF WEIGHTED NETWORKS

Advances in Complex Systems (Impact Factor: 0.65). 11/2011; 12(01). DOI: 10.1142/S0219525909002039

ABSTRACT In this paper we deal with the structural properties of weighted networks. Starting from an empirical analysis of a linguistic network, we analyze the differences between the statistical properties of a real and a shuffled network. We show that the scale-free degree distribution and the scale-free weight distribution are induced by the scale-free strength distribution, that is Zipf's law. We test the result on a scientific collaboration network, that is a social network, and we define a measure – the vertex selectivity – that can distinguish a real network from a shuffled network. We prove, via an ad hoc stochastic growing network with second order correlations, that this measure can effectively capture the correlations within the topology of the network.

0 Bookmarks
 · 
7 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we analyse the street network of London both in its primary and dual representation. To understand its properties, we consider three idealised models based on a grid, a static random planar graph and a growing random planar graph. Comparing the models and the street network, we find that the streets of London form a self-organising system whose growth is characterised by a strict interaction between the metrical and informational space. In particular, a principle of least effort appears to create a balance between the physical and the mental effort required to navigate the city.
    Physics of Condensed Matter 03/2009; · 1.28 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper studies the effect of linguistic constraints on the large scale organization of language. It describes the properties of linguistic networks built using texts of written language with the words randomized. These properties are compared to those obtained for a network built over the text in natural order. It is observed that the "random" networks too exhibit small-world and scale-free characteristics. They also show a high degree of clustering. This is indeed a surprising result - one that has not been addressed adequately in the literature. We hypothesize that many of the network statistics reported here studied are in fact functions of the distribution of the underlying data from which the network is built and may not be indicative of the nature of the concerned network.
    Computing Research Repository - CORR. 02/2011;

Full-text (2 Sources)

View
7 Downloads
Available from
May 23, 2014