Structural information content of networks: Graph entropy based on local vertex functionals

被引：29
|
作者：
Dehmer, Matthias [1 ]
Emmert-Streib, Frank [2 ,3 ]
机构：
[1] TU Vienna, Vienna Univ Technol, Inst Discrete Math & Geometry, A-1040 Vienna, Austria
[2] Univ Washington, Dept Biostat, Seattle, WA 98195 USA
[3] Univ Washington, Dept Genome Sci, Seattle, WA 98195 USA
来源：
COMPUTATIONAL BIOLOGY AND CHEMISTRY | 2008年 / 32卷 / 02期
关键词：
structural information content; graph entropy; information theory; gene networks; chemical graph theory;
D O I：
10.1016/j.compbiolchem.2007.09.007
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
In this paper we define the structural information content of graphs as their corresponding graph entropy. This definition is based on local vertex functionals obtained by calculating-spheres via the algorithm of Dijkstra. We prove that the graph entropy and, hence, the local vertex functionals can be computed with polynomial time complexity enabling the application of our measure for large graphs. In this paper we present numerical results for the graph entropy of chemical graphs and discuss resulting properties. (C) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码：131 / 138
页数：8
相关论文
共 50 条
  • [1] Information processing in complex networks: Graph entropy and information functionals
    Dehmer, Matthias
    APPLIED MATHEMATICS AND COMPUTATION, 2008, 201 (1-2) : 82 - 94
  • [2] Learning Graph Convolutional Networks Based on Quantum Vertex Information Propagation
    Bai, Lu
    Jiao, Yuhang
    Cui, Lixin
    Rossi, Luca
    Wang, Yue
    Yu, Philip S.
    Hancock, Edwin R.
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2023, 35 (02) : 1747 - 1760
  • [3] Learning Graph Convolutional Networks based on Quantum Vertex Information Propagation (Extended Abstract)
    Bai, Lu
    Jiao, Yuhang
    Cui, Lixin
    Rossi, Luca
    Wang, Yue
    Yu, Philip S.
    Hancock, Edwin R.
    2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022), 2022, : 3132 - 3133
  • [4] Usefulness of graph vertex complexity and class partial information content in explaining gas phase thermal entropy of chemical compounds
    Raychaudhury, Chandan
    Pal, Debnath
    JOURNAL OF MATHEMATICAL CHEMISTRY, 2020, 58 (05) : 887 - 892
  • [5] Usefulness of graph vertex complexity and class partial information content in explaining gas phase thermal entropy of chemical compounds
    Chandan Raychaudhury
    Debnath Pal
    Journal of Mathematical Chemistry, 2020, 58 : 887 - 892
  • [6] An information dissemination strategy in social networks based on graph and content analysis
    Huang, Jing
    EGYPTIAN INFORMATICS JOURNAL, 2025, 29
  • [7] RWE: A Random Walk Based Graph Entropy for the Structural Complexity of Directed Networks
    Zhang, Chong
    Deng, Cheng
    Fu, Luoyi
    Wang, Xinbing
    Chen, Guihai
    Zhou, Lei
    Zhou, Chenghu
    IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2024, 11 (02): : 2264 - 2278
  • [8] CLASSIFICATION OF STRUCTURAL BRAIN NETWORKS BASED ON INFORMATION DIVERGENCE OF GRAPH SPECTRA
    Dodonova, Yulia
    Korolev, Sergey
    Tkachev, Anna
    Petrov, Dmitry
    Zhukov, Leonid
    Belyaev, Mikhail
    2016 IEEE 26TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP), 2016,
  • [9] The structural information content of chemical networks
    Dehmer, Matthias
    Emmert-Streib, Frank
    ZEITSCHRIFT FUR NATURFORSCHUNG SECTION A-A JOURNAL OF PHYSICAL SCIENCES, 2008, 63 (3-4): : 155 - 158
  • [10] Network entropy using edge-based information functionals
    Aziz, Furqan
    Hancock, Edwin R.
    Wilson, Richard C.
    JOURNAL OF COMPLEX NETWORKS, 2020, 8 (03)
12345