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经典网络熵和参数网络熵之间的联系。

Connections between classical and parametric network entropies.

机构信息

Institute for Bioinformatics and Translational Research, UMIT, Hall in Tirol, Austria.

出版信息

PLoS One. 2011 Jan 5;6(1):e15733. doi: 10.1371/journal.pone.0015733.

DOI:10.1371/journal.pone.0015733
PMID:21246046
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3016402/
Abstract

This paper explores relationships between classical and parametric measures of graph (or network) complexity. Classical measures are based on vertex decompositions induced by equivalence relations. Parametric measures, on the other hand, are constructed by using information functions to assign probabilities to the vertices. The inequalities established in this paper relating classical and parametric measures lay a foundation for systematic classification of entropy-based measures of graph complexity.

摘要

本文探讨了图（或网络）复杂性的经典度量和参数度量之间的关系。经典度量基于等价关系诱导的顶点分解。另一方面，参数度量是通过使用信息函数为顶点分配概率来构建的。本文建立的将经典度量和参数度量联系起来的不等式为基于熵的图复杂性度量的系统分类奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab6f/3016402/04601770d6cf/pone.0015733.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab6f/3016402/874072aaabe1/pone.0015733.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab6f/3016402/eff75bfef4ce/pone.0015733.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab6f/3016402/c9614fd4de95/pone.0015733.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab6f/3016402/04601770d6cf/pone.0015733.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab6f/3016402/874072aaabe1/pone.0015733.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab6f/3016402/eff75bfef4ce/pone.0015733.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab6f/3016402/c9614fd4de95/pone.0015733.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab6f/3016402/04601770d6cf/pone.0015733.g004.jpg

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