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Wikipedia information flow analysis reveals the scale-free architecture of the semantic space.

Masucci AP, Kalampokis A, Eguíluz VM, Hernández-García E - PLoS ONE (2011)

Bottom Line: In particular we find that the cluster size distribution, representing the size of different semantic areas, is scale-free.Moreover the topology of the resulting semantic space is scale-free in the connectivity distribution and displays small-world properties.However its statistical properties do not allow a classical interpretation via a generative model based on a simple multiplicative process.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Física Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Científicas - Universitat de les Illes Balears, Palma de Mallorca, Spain.

ABSTRACT
In this paper we extract the topology of the semantic space in its encyclopedic acception, measuring the semantic flow between the different entries of the largest modern encyclopedia, Wikipedia, and thus creating a directed complex network of semantic flows. Notably at the percolation threshold the semantic space is characterised by scale-free behaviour at different levels of complexity and this relates the semantic space to a wide range of biological, social and linguistics phenomena. In particular we find that the cluster size distribution, representing the size of different semantic areas, is scale-free. Moreover the topology of the resulting semantic space is scale-free in the connectivity distribution and displays small-world properties. However its statistical properties do not allow a classical interpretation via a generative model based on a simple multiplicative process. After giving a detailed description and interpretation of the topological properties of the semantic space, we introduce a stochastic model of content-based network, based on a copy and mutation algorithm and on the Heaps' law, that is able to capture the main statistical properties of the analysed semantic space, including the Zipf's law for the word frequency distribution.

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Connectivity distribution of the semantic space.Out-degree distribution  (left panel) and in-degree distribution  (right panel) of the semantic network at the percolation threshold. In the insets the corresponding cumulative degree distributions  are displayed.
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pone-0017333-g002: Connectivity distribution of the semantic space.Out-degree distribution (left panel) and in-degree distribution (right panel) of the semantic network at the percolation threshold. In the insets the corresponding cumulative degree distributions are displayed.

Mentions: The out-degree and in-degree distribution of the network at the PT are scale-free with a very slow decay, characterised by exponents: and (see Fig. 2). The distributions are scale invariant until very large scales where a sharp cut-off appears, revealing that the SS is characterised by structures at all the scales. The giant component of the network has a directed diameter that is of the order of the logarithm of the cluster size. Moreover its average clustering coefficient is , that is larger than the clustering coefficient of a random network of the same size, , revealing local small-world properties of the SS [21].


Wikipedia information flow analysis reveals the scale-free architecture of the semantic space.

Masucci AP, Kalampokis A, Eguíluz VM, Hernández-García E - PLoS ONE (2011)

Connectivity distribution of the semantic space.Out-degree distribution  (left panel) and in-degree distribution  (right panel) of the semantic network at the percolation threshold. In the insets the corresponding cumulative degree distributions  are displayed.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3046238&req=5

pone-0017333-g002: Connectivity distribution of the semantic space.Out-degree distribution (left panel) and in-degree distribution (right panel) of the semantic network at the percolation threshold. In the insets the corresponding cumulative degree distributions are displayed.
Mentions: The out-degree and in-degree distribution of the network at the PT are scale-free with a very slow decay, characterised by exponents: and (see Fig. 2). The distributions are scale invariant until very large scales where a sharp cut-off appears, revealing that the SS is characterised by structures at all the scales. The giant component of the network has a directed diameter that is of the order of the logarithm of the cluster size. Moreover its average clustering coefficient is , that is larger than the clustering coefficient of a random network of the same size, , revealing local small-world properties of the SS [21].

Bottom Line: In particular we find that the cluster size distribution, representing the size of different semantic areas, is scale-free.Moreover the topology of the resulting semantic space is scale-free in the connectivity distribution and displays small-world properties.However its statistical properties do not allow a classical interpretation via a generative model based on a simple multiplicative process.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Física Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Científicas - Universitat de les Illes Balears, Palma de Mallorca, Spain.

ABSTRACT
In this paper we extract the topology of the semantic space in its encyclopedic acception, measuring the semantic flow between the different entries of the largest modern encyclopedia, Wikipedia, and thus creating a directed complex network of semantic flows. Notably at the percolation threshold the semantic space is characterised by scale-free behaviour at different levels of complexity and this relates the semantic space to a wide range of biological, social and linguistics phenomena. In particular we find that the cluster size distribution, representing the size of different semantic areas, is scale-free. Moreover the topology of the resulting semantic space is scale-free in the connectivity distribution and displays small-world properties. However its statistical properties do not allow a classical interpretation via a generative model based on a simple multiplicative process. After giving a detailed description and interpretation of the topological properties of the semantic space, we introduce a stochastic model of content-based network, based on a copy and mutation algorithm and on the Heaps' law, that is able to capture the main statistical properties of the analysed semantic space, including the Zipf's law for the word frequency distribution.

Show MeSH