Abstract
The Randic index is a well known topological index (TI) used in QSAR/QSPR studies to quantify the molecular structure represented by a graph. In this work we review some aspects of this TI with special emphasis on the generalizations introduced by Kier & Hall and more recently by Estrada. Next, we introduced a new generalization using a Markov chain in order to obtain a new family of TIs called the Markov-Randi indices of order k-th (1χk). Later, we applied these new indices to seek models useful to calculate numerical quality scores S(Lij) for network links Lij (connectivity) in known complex networks. The linear models obtained produced the following results in terms of overall accuracy for network re-construction: Metabolic networks (70.48%), Parasite-Host networks (90.86%), CoCoMac brain cortex co-activation network (81.59%), NW Spain Fasciolosis spreading network (99.04%). Spanish financial law network (71.83%). This work opens a new door to the computational re-evaluation of network connectivity quality (collation) in different complex systems.
Keywords: Brain cortex networks, complex networks, connectome, disease spreading networks, legal-social networks, markov chains, metabolic reactions networks, quantitative structure-property relationships, topological indices.
Current Bioinformatics
Title:Markov-Randic Indices for QSPR Re-Evaluation of Metabolic, Parasite- Host, Fasciolosis Spreading, Brain Cortex and Legal-Social Complex Networks
Volume: 8 Issue: 4
Author(s): Pablo Riera-Fernandez, Cristian R. Munteanu, Raquel Martin-Romalde, Aliuska Duardo-Sanchez and Humberto Gonzalez-Diaz
Affiliation:
Keywords: Brain cortex networks, complex networks, connectome, disease spreading networks, legal-social networks, markov chains, metabolic reactions networks, quantitative structure-property relationships, topological indices.
Abstract: The Randic index is a well known topological index (TI) used in QSAR/QSPR studies to quantify the molecular structure represented by a graph. In this work we review some aspects of this TI with special emphasis on the generalizations introduced by Kier & Hall and more recently by Estrada. Next, we introduced a new generalization using a Markov chain in order to obtain a new family of TIs called the Markov-Randi indices of order k-th (1χk). Later, we applied these new indices to seek models useful to calculate numerical quality scores S(Lij) for network links Lij (connectivity) in known complex networks. The linear models obtained produced the following results in terms of overall accuracy for network re-construction: Metabolic networks (70.48%), Parasite-Host networks (90.86%), CoCoMac brain cortex co-activation network (81.59%), NW Spain Fasciolosis spreading network (99.04%). Spanish financial law network (71.83%). This work opens a new door to the computational re-evaluation of network connectivity quality (collation) in different complex systems.
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Cite this article as:
Riera-Fernandez Pablo, Munteanu R. Cristian, Martin-Romalde Raquel, Duardo-Sanchez Aliuska and Gonzalez-Diaz Humberto, Markov-Randic Indices for QSPR Re-Evaluation of Metabolic, Parasite- Host, Fasciolosis Spreading, Brain Cortex and Legal-Social Complex Networks, Current Bioinformatics 2013; 8 (4) . https://dx.doi.org/10.2174/1574893611308040003
DOI https://dx.doi.org/10.2174/1574893611308040003 |
Print ISSN 1574-8936 |
Publisher Name Bentham Science Publisher |
Online ISSN 2212-392X |
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