graphlet在复杂网络中基于谱能量贡献的重要性排序

IF 0.9 Q3 COMPUTER SCIENCE, THEORY & METHODS
F. Safaei, F. K. Jahromi, S. Fathi
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引用次数: 1

摘要

网络递归分解是网络分析中广泛使用的一种方法,用于将网络结构分解为具有较少节点的小子图模式。这些模式被称为石墨(基序),它们的分析被认为是生物信息学中的一种常用方法。本文主要研究了网络中石墨烯重要性的评估,并提出了一种基于石墨烯对图能谱的贡献对其重要性进行排序的分析模型。此外,给出了计算石墨烯能量占图总能量的一般公式;然后根据图的小图估计图的能量值。对合成网络和真实网络的实证分析结果与理论结果一致,表明本文提出的分析模型可以根据给定图的石墨烯准确地估计图的结构特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graphlets importance ranking in complex networks based on the spectral energy contribution
ABSTRACT Recursive decomposition of networks is a widely used approach in network analysis for factorization of network structure into small subgraph patterns with few nodes. These patterns are called graphlets (motifs), and their analysis is considered as a common approach in bioinformatics. This paper focuses on evaluating the importance of graphlets in networks and proposes a new analytical model for ranking the graphlets importance based on their contribution to the graph energy spectrum. Besides, a general formula is provided to calculate the graphlet energy contribution to the total energy of a graph; then the energy value of the graph is estimated based on its graphlets. The results of the empirical analysis of synthetic and real networks are consistent with the theoretical results and suggest that the proposed analytical model can accurately estimate the structural features of a given graph based on its graphlets.
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来源期刊
International Journal of Computer Mathematics: Computer Systems Theory
International Journal of Computer Mathematics: Computer Systems Theory Computer Science-Computational Theory and Mathematics
CiteScore
1.80
自引率
0.00%
发文量
11
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