a large scale analysis of information-theoretic network complexity measures using chemical structures大规模的信息理论分析网络使用化学结构复杂性度量.pdfVIP

A Large Scale Analysis of Information-Theoretic Network Complexity Measures Using Chemical Structures 1 2 3 1 Matthias Dehmer *, Nicola Barbarini , Kurt Varmuza , Armin Graber 1 Institute for Bioinformatics and Translational Research, UMIT, Hall in Tyrol, Austria, 2 Department of Computer Engineering and Systems Science, University of Pavia, Pavia, Italy, 3 Institute of Chemical Engineering, Laboratory for Chemometrics, Vienna University of Technology, Vienna, Austria Abstract This paper aims to investigate information-theoretic network complexity measures which have already been intensely used in mathematical- and medicinal chemistry including drug design. Numerous such measures have been developed so far but many of them lack a meaningful interpretation, e.g., we want to examine which kind of structural information they detect. Therefore, our main contribution is to shed light on the relatedness between some selected information measures for graphs by performing a large scale analysis using chemical networks. Starting from several sets containing real and synthetic chemical structures represented by graphs, we study the relatedness between a classical (partition-based) complexity measure called the topological information content of a graph and some others inferred by a different paradigm leading to partition-independent measures. Moreover, we evaluate the uniqueness of network complexity measures numerically. Generally, a high uniqueness is an important and desirable property when designing novel topological descriptors having the potential to be applied to large chemical databases. Citation: Dehmer M, Barbarini N, Varmuza K, Graber A (2009) A Large Scale Analysis of Information-Theoretic Network Complexity Measures Using Chemical Structur