Entropy 2014, 16(1), 557-566; doi:10.3390/e16010557
Article

Properties of Branch Length Similarity Entropy on the Network in Rk

National Institute for Mathematical Sciences, 70, Yuseong-daero 1689 beon-gil, Yuseon-gu, Daejeon, Korea
* Author to whom correspondence should be addressed.
Received: 7 November 2013; in revised form: 20 December 2013 / Accepted: 3 January 2014 / Published: 16 January 2014
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Abstract: Branching network is one of the most universal phenomena in living or non-living systems, such as river systems and the bronchial trees of mammals. To topologically characterize the branching networks, the Branch Length Similarity (BLS) entropy was suggested and the statistical methods based on the entropy have been applied to the shape identification and pattern recognition. However, the mathematical properties of the BLS entropy have not still been explored in depth because of the lack of application and utilization requiring advanced mathematical understanding. Regarding the mathematical study, it was reported, as a theorem, that all BLS entropy values obtained for simple networks created by connecting pixels along the boundary of a shape are exactly unity when the shape has infinite resolution. In the present study, we extended the theorem to the network created by linking infinitely many nodes distributed on the bounded or unbounded domain in Rk for k ≥ 1. We proved that all BLS entropies of the nodes in the network go to one as the number of nodes, n, goes to infinite and its convergence rate is 1 - O(1= ln n), which was confirmed by the numerical tests.
Keywords: branch length similarity entropy; BLS entropy; convergence rate

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MDPI and ACS Style

Kwon, O.S.; Lee, S.-H. Properties of Branch Length Similarity Entropy on the Network in Rk. Entropy 2014, 16, 557-566.

AMA Style

Kwon OS, Lee S-H. Properties of Branch Length Similarity Entropy on the Network in Rk. Entropy. 2014; 16(1):557-566.

Chicago/Turabian Style

Kwon, Oh S.; Lee, Sang-Hee. 2014. "Properties of Branch Length Similarity Entropy on the Network in Rk." Entropy 16, no. 1: 557-566.

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