| Alkiviadis Kalampokis|
(Alternative names for this author)
|Co-authors||Adolfo Paolo Masucci, Adolfo Paolo P. Masucci, Eguiluz V.M., Emilio Hernández-García, Hernandez-Garcia E., Masucci A.P., Victor Martínez M. Eguíluz, Víctor Martínez Eguíluz|
|Authorship||Publications (3), datasets (0), tools (0)|
|Citations||Total (0), average (0), median (0), max (0), min (0)|
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Alkiviadis Kalampokis is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Extracting directed information flow networks: An application to genetics and semantics||Physical Review E - Statistical, Nonlinear, and Soft Matter Physics||English||2011||We introduce a general method to infer the directional information flow between populations whose elements are described by n-dimensional vectors of symbolic attributes. The method is based on the Jensen-Shannon divergence and on the Shannon entropy and has a wide range of application. We show here the results of two applications: first we extract the network of genetic flow between meadows of the seagrass Poseidonia oceanica, where the meadow elements are specified by sets of microsatellite markers, and then we extract the semantic flow network from a set of Wikipedia pages, showing the semantic channels between different areas of knowledge.||0||0|
|Wikipedia information flow analysis reveals the scale-free architecture of the semantic space||PloS one||English||2011||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.||0||0|