A structural approach to disentangle the visualization of bipartite biological networks

García-Algarra, Javier; Pastor, Juan Manuel; López, Mary Luz Mouronte; Galeano, Javier

doi:10.1101/192013

Cited by 1 publication

(1 citation statement)

References 39 publications

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“…In addition to homogeneous networks that appear in biology, bipartite networks are also quite frequent: gene-protein, host-pathogen, predator-prey. In [56], two new visualization types are proposed that exploit the structural properties of these networks to improve readability. The basis of these methods is the core decomposition of the bipartite graph.…”

Section: Physics Biology and Ecologymentioning

confidence: 99%

The core decomposition of networks: theory, algorithms and applications

et al. 2019

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The core decomposition of networks has attracted significant attention due to its numerous applications in real-life problems. Simply stated, the core decomposition of a network (graph) assigns to each graph node v, an integer number c(v) (the core number), capturing how well v is connected with respect to its neighbors. This concept is strongly related to the concept of graph degeneracy, which has a long history in Graph Theory. Although the core decomposition concept is extremely simple, there is an enormous interest in the topic from diverse application domains, mainly because it can be used to analyze a network in a simple and concise manner by quantifying the significance of graph nodes. Therefore, there exists a respectable number of research works that either propose efficient algorithmic techniques under different settings and graph types or apply the concept to another problem or scientific area. Based on this large interest in the topic, in this survey, we perform an in-depth discussion of core decomposition, focusing mainly on: i) the basic theory and fundamental concepts, ii) the algorithmic techniques proposed for computing it efficiently under different settings, and iii) the applications that can benefit significantly from it.

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Section: Physics Biology and Ecologymentioning

confidence: 99%