A Structural Approach to Disentangle the Visualization of Bipartite Biological Networks

Abstract

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.

Multilevel Coarsening for Interactive Visualization of Large Bipartite Networks

“…The visual encoding adopts the usual approach of presenting two vertical lists of nodes, laid out in parallel. Other approaches are also employed to assist visualization of bipartite graphs, as in Garcia-Algarra et al ( 2018 ), who use a k-core decomposition to identify and aggregate groups of nodes that share connectivity properties in order to simplify the network structure. The rationale of aggregating groups of nodes that share connectivity properties is also at the core of multilevel coarsening, employed in this paper.…”

“…Effectively conveying the relevant topological information of large-scale networks is challenging in interactive visualization (Tang et al, 2015 ; Staudt et al, 2016 ), and even more so in handling bipartite networks, in view of their peculiar organization and inherent topological complexity. A few recent contributions discuss strategies for interactive visualization of such networks (Chan et al, 2018 ; Garcia-Algarra et al, 2018 ; Pezzotti et al, 2018 ; Steinbock et al, 2018 ; Sun et al, 2019 ; Zhao et al, 2019 ; Waldner et al, 2020 ), as further discussed in Section 3.…”

Prediction of collapse process and tipping points for mutualistic and competitive networks with k-core method

“…The k-core, which is the network subgraph with degree of all nodes at least k, provides a way to effectively capture the topological invariance of the interaction network between species and is widely used. [16][17][18] Based on k-core decomposition, Garcia et al [19,20] proposed a method to rank key species in mutualistic networks and a structured way to visualize bipartite ecological networks. Using k-core as a predictor of mutualistic ecosystem structure collapse, Morone et al [21] revealed that the root cause of system collapse is the extinction of species located in the largest k-core of the network.…”