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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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Cluster size distribution of the semantic space.Cluster size distribution  of the semantic network at the percolation threshold. In the inset we show the cumulative distribution .
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pone-0017333-g001: Cluster size distribution of the semantic space.Cluster size distribution of the semantic network at the percolation threshold. In the inset we show the cumulative distribution .

Mentions: The SS network reaches its PT at approximately 362000 pages, when the two main clusters merge to form a giant cluster of 57800 pages. At the PT the network has links with an average degree . The very large average degree means that the clusters are very densely connected. The network is composed by 44500 disconnected clusters showing scale invariant cluster size distribution, , with a fat tail (Fig. 1).


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)

Cluster size distribution of the semantic space.Cluster size distribution  of the semantic network at the percolation threshold. In the inset we show the cumulative distribution .
© Copyright Policy
Related In: Results  -  Collection

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

pone-0017333-g001: Cluster size distribution of the semantic space.Cluster size distribution of the semantic network at the percolation threshold. In the inset we show the cumulative distribution .
Mentions: The SS network reaches its PT at approximately 362000 pages, when the two main clusters merge to form a giant cluster of 57800 pages. At the PT the network has links with an average degree . The very large average degree means that the clusters are very densely connected. The network is composed by 44500 disconnected clusters showing scale invariant cluster size distribution, , with a fat tail (Fig. 1).

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