Paper Info
Title
A New Approach for Discovering and Quantifying Hierarchical Structure of Complex Networks
Abstract
Biological robustness has been understood in many different ways. One of them is based on analysis of topological structures such as modularity, and hierarchy. While modularity has been studied in various areas intensively, only a few methods have been proposed to study hierarchical structure of networks. In this paper we propose a new algorithm to discover and quantify hierarchical structure of complex networks. This new algorithm identifies nodes on the top layer of hierarchical structure based on betweenness calculation among links, and groups the rest of nodes according to the distance from nodes on the top layer in order to locate them in different layers. The rearranged structure is quantified to represent the strength of hierarchy structure. In addition, we show the difference between hierarchy and modularity that have been regarded as similar properties.
Year
DOI
Venue
2008
10.1109/ICAS.2008.13
ICAS
Keywords
Field
DocType
different way,betweenness calculation,complex networks,hierarchical structure,hierarchy structure,new algorithm,rearranged structure,new approach,biological robustness,different layer,topological structure,top layer,quantifying hierarchical structure,hierarchy,biological systems,graph theory,computer networks,betweenness centrality,information science,biology,visualization,complex network,protocols,modularity,topology,robustness
Modularity (networks),Computer science,Girvan–Newman algorithm,Hierarchical clustering of networks,Theoretical computer science,Hierarchical network model,Complex network,Hierarchy,Clique percolation method,Modularity
Conference
Citations 
PageRank 
References 
4
0.68
3
Authors
3
Name
Order
Citations
PageRank
Suyong Eum19911.34
Shin'ichi Arakawa27724.34
Masayuki Murata31615239.01