Literature DB >> 18243802

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

Matthias Dehmer1, Frank Emmert-Streib.   

Abstract

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 j-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.

Mesh:

Year:  2007        PMID: 18243802     DOI: 10.1016/j.compbiolchem.2007.09.007

Source DB:  PubMed          Journal:  Comput Biol Chem        ISSN: 1476-9271            Impact factor:   2.877


  9 in total

Review 1.  Trends in information theory-based chemical structure codification.

Authors:  Stephen J Barigye; Yovani Marrero-Ponce; Facundo Pérez-Giménez; Danail Bonchev
Journal:  Mol Divers       Date:  2014-04-05       Impact factor: 2.943

2.  Novel topological descriptors for analyzing biological networks.

Authors:  Matthias M Dehmer; Nicola N Barbarini; Kurt K Varmuza; Armin A Graber
Journal:  BMC Struct Biol       Date:  2010-06-17

3.  New polynomial-based molecular descriptors with low degeneracy.

Authors:  Matthias Dehmer; Laurin A J Mueller; Armin Graber
Journal:  PLoS One       Date:  2010-07-30       Impact factor: 3.240

4.  Process-driven inference of biological network structure: feasibility, minimality, and multiplicity.

Authors:  Guanyu Wang; Yongwu Rong; Hao Chen; Carl Pearson; Chenghang Du; Rahul Simha; Chen Zeng
Journal:  PLoS One       Date:  2012-07-18       Impact factor: 3.240

5.  Towards information inequalities for generalized graph entropies.

Authors:  Lavanya Sivakumar; Matthias Dehmer
Journal:  PLoS One       Date:  2012-06-08       Impact factor: 3.240

6.  Unravelling personalized dysfunctional gene network of complex diseases based on differential network model.

Authors:  Xiangtian Yu; Tao Zeng; Xiangdong Wang; Guojun Li; Luonan Chen
Journal:  J Transl Med       Date:  2015-06-13       Impact factor: 5.531

7.  Adjusting protein graphs based on graph entropy.

Authors:  Sheng-Lung Peng; Yu-Wei Tsay
Journal:  BMC Bioinformatics       Date:  2014-12-03       Impact factor: 3.169

8.  A large scale analysis of information-theoretic network complexity measures using chemical structures.

Authors:  Matthias Dehmer; Nicola Barbarini; Kurt Varmuza; Armin Graber
Journal:  PLoS One       Date:  2009-12-15       Impact factor: 3.240

9.  Entropy bounds for hierarchical molecular networks.

Authors:  Matthias Dehmer; Stephan Borgert; Frank Emmert-Streib
Journal:  PLoS One       Date:  2008-08-28       Impact factor: 3.240

  9 in total

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