Markov Entropy Centrality: Chemical, Biological, Crime, and Legislative Networks

C. R. Munteanu (), J. Dorado (), Alejandro Pazos-Sierra (), F. Prado-Prado (), L. G. Pérez-Montoto (), S. Vilar (), F. M. Ubeira (), A. Sanchez-Gonzaléz (), M. Cruz-Monteagudo (), S. Arrasate (), N. Sotomayor (), E. Lete (), A. Duardo-Sánchez (), A. Díaz-López (), G. Patlewicz () and H. González-Díaz ()
Additional contact information
C. R. Munteanu: University of A Coruña, Department of Information and Communication Technologies, Computer Science Faculty
J. Dorado: University of A Coruña, Department of Information and Communication Technologies, Computer Science Faculty
Alejandro Pazos-Sierra: University of A Coruña, Department of Information and Communication Technologies, Computer Science Faculty
F. Prado-Prado: University of Santiago de Compostela, Faculty of Pharmacy
L. G. Pérez-Montoto: University of Santiago de Compostela, Faculty of Pharmacy
S. Vilar: University of Santiago de Compostela, Faculty of Pharmacy
F. M. Ubeira: University of Santiago de Compostela, Department of Microbiology and Parasitology, Faculty of Pharmacy
A. Sanchez-Gonzaléz: University of Santiago de Compostela, Department of Inorganic Chemistry, Faculty of Pharmacy
M. Cruz-Monteagudo: UCLV, CEQA, Faculty of Chemistry and Pharmacy
S. Arrasate: University of the Basque Country/Euskal Herriko Unibertsitatea, Department of Organic Chemistry II, Faculty of Science and Technology
N. Sotomayor: University of Santiago de Compostela, Department of Special Public Law, Faculty of Law
E. Lete: University of Santiago de Compostela, Department of Special Public Law, Faculty of Law
A. Duardo-Sánchez: Institute for Health and Consumer Protection (IHPC), Joint Research Centre (JRC), EuropeanCommission
A. Díaz-López: Institute for Health and Consumer Protection (IHPC), Joint Research Centre (JRC), EuropeanCommission
G. Patlewicz: DuPont Haskell Global Centers for Health and Environmental Sciences
H. González-Díaz: University of Santiago de Compostela, Department of Microbiology and Parasitology

Chapter Chapter 9 in Towards an Information Theory of Complex Networks, 2011, pp 199-258 from Springer

Abstract: Abstract In this chapter, we propose the study of multiple systems using node centrality or connectedness information measures derived from a Graph or Complex Network. The information is quantified in terms of the Entropy centrality kC θ(j) of the jth parts or states (nodes) of a Markov Chain associated with the system, represented by a network graph. The procedure is standard for all systems despite the complexity of the system. First, we define the phenomena to study, ranging from molecular systems composed by single molecules (drug activity, drug toxicity), multiple molecules (networks of chemical reactions), and macromolecules (DNA–drug interaction, protein function), to ecological systems (bacterial co-aggregation), or social systems (criminal causation, legislative productivity). Second, we collect several cases from literature (drugs, chemical reactions, proteins, bacterial species, or criminal cases). Next, we classify the cases in at least two different groups (active/nonactive drugs, enantioselective/non-enantioselective reactions, functional/nonfunctional proteins, co-aggregating/non-co-aggregating bacteria, or crime/noncrime cause, efficient/nonefficient law). After that, we represent the interconnectivity of the discrete parts of the system (atoms, amino acids, reactants, bacteria species, or people) as a graph or network. The Markov Chain theory is used to calculate the entropy of the system for nodes placed at different distances. Finally, we aim to both derive and validate a classification model using the entropy values as input variables and the classification of cases as the output variables. The model is used to predict the probability with which a case presents the studied property. The present work proposes the entropy of a Markov Chain associated with a network or graph to be used as a universal quantity in pattern recognition regardless the chemical, biological, social, or other nature of the systems under study.

Keywords: Bacteria co-aggregation; Chiral reaction; Complex network; Criminal causation; Drug design; Ecology; Entropy; Graph theory; Markov chain; Organic synthesis; Parasite–host interaction; Political legislative networks; Proteomics (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-0-8176-4904-3_9

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DOI: 10.1007/978-0-8176-4904-3_9

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