Paper Info
Title
Graphical Classification in Multi-Centrality-Index Diagrams for Complex Chemical Networks.
Abstract
Various sizes of chemical reaction network exist, from small graphs of linear networks with several inorganic species to huge complex networks composed of protein reactions or metabolic systems. Huge complex networks of organic substrates have been well studied using statistical properties such as degree distributions. However, when the size is relatively small, statistical data suffers from significant errors coming from irregular effects by species, and a macroscopic analysis is frequently unsuccessful. In this study, we demonstrate a graphical classification method for chemical networks that contain tens of species. Betweenness and closeness centrality indices of a graph can create a two-dimensional diagram with information of node distribution for a complex chemical network. This diagram successfully reveals systematic sharing of roles among species as a semi-statistical property in chemical reactions, and distinguishes it from the ones in random networks, which has no functional node distributions. This analytical approach is applicable for rapid and approximate understanding of complex chemical network systems such as plasma-enhanced reactions as well as visualization and classification of other graphs.
Year
DOI
Venue
2017
10.3390/sym9120309
SYMMETRY-BASEL
Keywords
Field
DocType
chemical reaction network,centrality index,statistical analysis,random graph
Graph,Combinatorics,Random graph,Visualization,Centrality,Theoretical computer science,Diagram,Betweenness centrality,Complex network,Mathematics,Statistical analysis
Journal
Volume
Issue
ISSN
9
12
2073-8994
Citations 
PageRank 
References 
0
0.34
1
Authors
4
Name
Order
Citations
PageRank
Yasutaka Mizui100.34
Tetsuya Kojima200.34
Shigeyuki Miyagi3163.77
Osamu Sakai401.01