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A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks
Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualizati...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3707208/ https://www.ncbi.nlm.nih.gov/pubmed/23864855 http://dx.doi.org/10.1155/2013/920325 |
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author | Hruz, Tomas Wyss, Markus Lucas, Christoph Laule, Oliver von Rohr, Peter Zimmermann, Philip Bleuler, Stefan |
author_facet | Hruz, Tomas Wyss, Markus Lucas, Christoph Laule, Oliver von Rohr, Peter Zimmermann, Philip Bleuler, Stefan |
author_sort | Hruz, Tomas |
collection | PubMed |
description | Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs. |
format | Online Article Text |
id | pubmed-3707208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-37072082013-07-17 A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks Hruz, Tomas Wyss, Markus Lucas, Christoph Laule, Oliver von Rohr, Peter Zimmermann, Philip Bleuler, Stefan Adv Bioinformatics Research Article Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs. Hindawi Publishing Corporation 2013 2013-06-26 /pmc/articles/PMC3707208/ /pubmed/23864855 http://dx.doi.org/10.1155/2013/920325 Text en Copyright © 2013 Tomas Hruz et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Hruz, Tomas Wyss, Markus Lucas, Christoph Laule, Oliver von Rohr, Peter Zimmermann, Philip Bleuler, Stefan A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks |
title | A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks |
title_full | A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks |
title_fullStr | A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks |
title_full_unstemmed | A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks |
title_short | A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks |
title_sort | multilevel gamma-clustering layout algorithm for visualization of biological networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3707208/ https://www.ncbi.nlm.nih.gov/pubmed/23864855 http://dx.doi.org/10.1155/2013/920325 |
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