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Advances in Bioinformatics
Volume 2013 (2013), Article ID 920325, 10 pages
http://dx.doi.org/10.1155/2013/920325
Research Article

A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks

1Institute of Theoretical Computer Science, ETH Zurich, 8092 Zurich, Switzerland
2NEBION AG, Hohlstraße 515, 8048 Zurich, Switzerland

Received 12 April 2013; Accepted 7 June 2013

Academic Editor: Guohui Lin

Copyright © 2013 Tomas Hruz et al. 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.

Abstract

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.