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arXiv 1202.5685cs.ITmath.IT
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信息不等式与广义图熵

Information inequalities and Generalized Graph Entropies

Lavanya Sivakumar, Matthias Dehmer

摘要:

在这篇文章中,我们讨论的问题,建立网络结构评估的信息措施之间的关系。两种类型的熵为基础的措施,即香农熵和它的推广,雷尼熵已被认为是这项研究。我们的主要结果涉及建立正式的关系,在隐式不等式的形式,这两种措施时,定义的图。此外,我们还陈述和证明了连接经典的基于划分的图熵和基于功能的熵测度的不等式。此外,对特殊的图类,还导出了几个显式不等式。

英文摘要:

In this article, we discuss the problem of establishing relations between information measures assessed for network structures. Two types of entropy based measures namely, the Shannon entropy and its generalization, the Rényi entropy have been considered for this study. Our main results involve establishing formal relationship, in the form of implicit inequalities, between these two kinds of measures when defined for graphs. Further, we also state and prove inequalities connecting the classical partition-based graph entropies and the functional-based entropy measures. In addition, several explicit inequalities are derived for special classes of graphs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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