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Volume 2018, Article ID 6204947, 11 pages
Research Article

A Structural Approach to Disentangle the Visualization of Bipartite Biological Networks

1Centro Universitario de Tecnología y Arte Digital (U-TAD), Las Rozas, Spain
2Complex Systems Group, Universidad Politecnica de Madrid, Madrid, Spain
3ETSIAAB, Departamento de Ingenieria Agroforestal, Universidad Politecnica de Madrid, Madrid, Spain
4Computer Science Department, Universidad Francisco de Vitoria, Madrid, Spain

Correspondence should be addressed to J. Garcia-Algarra; moc.dat-u.evil@arragla.reivaj and J. Galeano; se.mpu@onaelag.reivaj

Received 21 November 2017; Accepted 29 January 2018; Published 28 February 2018

Academic Editor: Philippe Bogaerts

Copyright © 2018 J. Garcia-Algarra 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.


Interactions between two different guilds of entities are pervasive in biology. They may happen at molecular level, like in a diseasome, or amongst individuals linked by biotic relationships, such as mutualism or parasitism. These sets of interactions are complex bipartite networks. Visualization is a powerful tool to explore and analyze them, but the most common plots, the bipartite graph and the interaction matrix, become rather confusing when working with real biological networks. We have developed two new types of visualization which exploit the structural properties of these networks to improve readability. A technique called k-core decomposition identifies groups of nodes that share connectivity properties. With the results of this analysis it is possible to build a plot based on information reduction (polar plot) and another which takes the groups as elementary blocks for spatial distribution (ziggurat plot). We describe the applications of both plots and the software to create them.