Heat flow-thermodynamic depth complexity in directed networks
作者： Francisco Escolano , Boyan Bonev , Edwin R. Hancock
DOI: 10.1007/978-3-642-34166-3_21
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摘要: In this paper we extend the heat diffusion-thermodynamic depth approach for undirected networks/graphs to directed graphs. This extension is motivated by need measure complexity of structural patterns encoded It consists of: a) analyzing and characterizing diffusion traces in graphs, b) extending thermodynamic framework capture second-order variability networks. our experiments characterize several networks derived from different natural languages. We show that proposed finds differences between languages are blind classical analysis degree distributions.
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