Graphical Classification in Multi-Centrality-Index Diagrams for Complex Chemical Networks
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 d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-12-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/9/12/309 |
| _version_ | 1811184786851495936 |
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| author | Yasutaka Mizui Tetsuya Kojima Shigeyuki Miyagi Osamu Sakai |
| author_facet | Yasutaka Mizui Tetsuya Kojima Shigeyuki Miyagi Osamu Sakai |
| author_sort | Yasutaka Mizui |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T13:18:22Z |
| format | Article |
| id | doaj.art-ac3ca7f1bdfd4a7f83847317a30a3864 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T13:18:22Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-ac3ca7f1bdfd4a7f83847317a30a38642022-12-22T04:22:19ZengMDPI AGSymmetry2073-89942017-12-0191230910.3390/sym9120309sym9120309Graphical Classification in Multi-Centrality-Index Diagrams for Complex Chemical NetworksYasutaka Mizui0Tetsuya Kojima1Shigeyuki Miyagi2Osamu Sakai3Electronic Systems Engineering, The University of Shiga Prefecture, 2500 Hassakacho, Hikone, Shiga 522-8533, JapanElectronic Systems Engineering, The University of Shiga Prefecture, 2500 Hassakacho, Hikone, Shiga 522-8533, JapanElectronic Systems Engineering, The University of Shiga Prefecture, 2500 Hassakacho, Hikone, Shiga 522-8533, JapanElectronic Systems Engineering, The University of Shiga Prefecture, 2500 Hassakacho, Hikone, Shiga 522-8533, JapanVarious 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.https://www.mdpi.com/2073-8994/9/12/309chemical reaction networkcentrality indexstatistical analysisrandom graph |
| spellingShingle | Yasutaka Mizui Tetsuya Kojima Shigeyuki Miyagi Osamu Sakai Graphical Classification in Multi-Centrality-Index Diagrams for Complex Chemical Networks Symmetry chemical reaction network centrality index statistical analysis random graph |
| title | Graphical Classification in Multi-Centrality-Index Diagrams for Complex Chemical Networks |
| title_full | Graphical Classification in Multi-Centrality-Index Diagrams for Complex Chemical Networks |
| title_fullStr | Graphical Classification in Multi-Centrality-Index Diagrams for Complex Chemical Networks |
| title_full_unstemmed | Graphical Classification in Multi-Centrality-Index Diagrams for Complex Chemical Networks |
| title_short | Graphical Classification in Multi-Centrality-Index Diagrams for Complex Chemical Networks |
| title_sort | graphical classification in multi centrality index diagrams for complex chemical networks |
| topic | chemical reaction network centrality index statistical analysis random graph |
| url | https://www.mdpi.com/2073-8994/9/12/309 |
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