Complex Network Growing Model Using Downlink Motifs - Couverture souple

F. Al Musawi, Ahmad

 
9783330842205: Complex Network Growing Model Using Downlink Motifs
Couverture souple
ISBN 10 : 3330842202 ISBN 13 : 9783330842205
Editeur : Noor Publishing, 2016

Synopsis

Understanding the underlying architecture of biological networks has been one of the major goals in systems biology and bioinformatics as it can provide insights in disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs, which are small subgraphs of specific types and appear more abundantly in GRNs than in other randomized networks. In fact, such motifs are considered to be the building blocks of complex networks and they help achieve the underlying robustness demonstrated by most biological networks. The goal of this thesis is to design biological network growing models. As the motif distribution in networks grown using preferential attachment based algorithms do not match that of the GRNs seen in model organisms like E.Coli and yeast,we hypothesize that such models at a single node level may not properly reproduce the observed degree and motif distributions of biological networks. Hence, we propose a new network growing algorithm wherein the idea is to grow the network one motif at a time.The accuracy of our algorithm was evaluated and show better performance than existing network growing models.

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Présentation de l'éditeur

Understanding the underlying architecture of biological networks has been one of the major goals in systems biology and bioinformatics as it can provide insights in disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs, which are small subgraphs of specific types and appear more abundantly in GRNs than in other randomized networks. In fact, such motifs are considered to be the building blocks of complex networks and they help achieve the underlying robustness demonstrated by most biological networks. The goal of this thesis is to design biological network growing models. As the motif distribution in networks grown using preferential attachment based algorithms do not match that of the GRNs seen in model organisms like E.Coli and yeast,we hypothesize that such models at a single node level may not properly reproduce the observed degree and motif distributions of biological networks. Hence, we propose a new network growing algorithm wherein the idea is to grow the network one motif at a time.The accuracy of our algorithm was evaluated and show better performance than existing network growing models.

Biographie de l'auteur

I am a lecturer in computer science department, college of computer science and mathematics, university of Thi Qar, Iraq. I have an M.Sc in computer science from Virginia Commonwealth University, United States of America. My research interests are on complex networks understanding and modelling, data mining and social network analysis.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Noor Publishing
Date d'édition
2016
Langue
anglais
ISBN 10 
3330842202
ISBN 13 
9783330842205
Reliure
Broché
Nombre de pages
88
Coordonnées du fabricant
non disponible
Personne responsable
non disponible