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分层分子网络的熵界

Entropy bounds for hierarchical molecular networks.

作者信息

Dehmer Matthias, Borgert Stephan, Emmert-Streib Frank

机构信息

Institute of Discrete Mathematics and Geometry, Vienna University of Technology, Vienna, Austria.

出版信息

PLoS One. 2008 Aug 28;3(8):e3079. doi: 10.1371/journal.pone.0003079.

Abstract

In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a recently introduced measure to determine the topological entropy of non-hierarchical networks, we provide bounds for estimating the entropy of hierarchical graphs. Apart from bounds to estimate the entropy of a single hierarchical graph, we see that the derived bounds can also be used for characterizing graph classes. Our contribution is an important extension to previous results about the entropy of non-hierarchical networks because for practical applications hierarchical networks are playing an important role in chemistry and biology. In addition to the derivation of the entropy bounds, we provide a numerical analysis for two special graph classes, rooted trees and generalized trees, and demonstrate hereby not only the computational feasibility of our method but also learn about its characteristics and interpretability with respect to data analysis.

摘要

在本文中,我们推导了分层网络的熵界。更确切地说,从最近引入的一种用于确定非分层网络拓扑熵的度量出发,我们给出了用于估计分层图熵的界。除了用于估计单个分层图熵的界之外,我们发现所推导的界还可用于刻画图类。我们的贡献是对先前关于非分层网络熵的结果的重要扩展,因为在实际应用中,分层网络在化学和生物学中发挥着重要作用。除了推导熵界之外,我们还对两种特殊的图类——根树和广义树进行了数值分析,从而不仅证明了我们方法的计算可行性,还了解了其在数据分析方面的特性和可解释性。

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