Graphs are often used to model relationships between entities. The identification and visualization of clusters in graphs enable insight discovery in many application areas, such as life sciences and social sciences. Force-directed graph layouts promote the visual saliency of clusters, as they bring adjacent nodes closer together, and push non-adjacent nodes apart. At the same time, matrices can effectively show clusters when a suitable row/column ordering is applied, but are less appealing to untrained users not providing an intuitive node-link metaphor. It is thus worth exploring layouts combining the strengths of the node-link metaphor and node ordering. In this work, we study the impact of node ordering on the visual saliency of clusters in orderable node-link diagrams, namely radial diagrams, arc diagrams and symmetric arc diagrams. Through a crowdsourced controlled experiment, we show that users can count clusters consistently more accurately, and to a large extent faster, with orderable node-link diagrams than with three state-of-the art force-directed layout algorithms, i.e., 'Linlog', 'Backbone' and 'sfdp'. The measured advantage is greater in case of low cluster separability and/or low compactness. A free copy of this paper and all supplemental materials are available at https://osf.io/kc3dg/.

Abstract In the past few years, large high‐resolution displays (LHRDs) have attracted considerable attention from researchers, industries and application areas that increasingly rely on data‐driven decision‐making. An up‐to‐date survey on the use of LHRDs for interactive data visualization seems warranted to summarize how new solutions meet the characteristics and requirements of LHRDs and take advantage of their unique benefits. In this survey, we start by defining LHRDs and outlining the consequence of LHRD environments on interactive visualizations in terms of more pixels, space, users and devices. Then, we review related literature along the four axes of visualization, interaction, evaluation studies and applications. With these four axes, our survey provides a unique perspective and covers a broad range of aspects being relevant when developing interactive visual data analysis solutions for LHRDs. We conclude this survey by reflecting on a number of opportunities for future research to help the community take up the still‐open challenges of interactive visualization on LHRDs.

Abstract Modelling relationship between entities in real‐world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real‐world data sets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those developing systems across application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks and analytic techniques from within application domains. This report also identifies the outstanding challenges for multilayer graph visualization and suggests future research directions for addressing them.

BACKGROUND: Recent advances in high-throughput sequencing allow for much deeper exploitation of natural and engineered microbial communities, and to unravel so-called "microbial dark matter" (microbes that until now have evaded cultivation). Metagenomic analyses result in a large number of genomic fragments (contigs) that need to be grouped (binned) in order to reconstruct draft microbial genomes. While several contig binning algorithms have been developed in the past 2 years, they often lack consensus. Furthermore, these software tools typically lack a provision for the visualization of data and bin characteristics. RESULTS: We present ICoVeR, the Interactive Contig-bin Verification and Refinement tool, which allows the visualization of genome bins. More specifically, ICoVeR allows curation of bin assignments based on multiple binning algorithms. Its visualization window is composed of two connected and interactive main views, including a parallel coordinates view and a dimensionality reduction plot. To demonstrate ICoVeR's utility, we used it to refine disparate genome bins automatically generated using MetaBAT, CONCOCT and MyCC for an anaerobic digestion metagenomic (AD microbiome) dataset. Out of 31 refined genome bins, 23 were characterized with higher completeness and lower contamination in comparison to their respective, automatically generated, genome bins. Additionally, to benchmark ICoVeR against a previously validated dataset, we used Sharon's dataset representing an infant gut metagenome. CONCLUSIONS: ICoVeR is an open source software package that allows curation of disparate genome bins generated with automatic binning algorithms. It is freely available under the GPLv3 license at https://git.list.lu/eScience/ICoVeR . The data management and analytical functions of ICoVeR are implemented in R, therefore the software can be easily installed on any system for which R is available. Installation and usage guide together with the example files ready to be visualized are also provided via the project wiki. ICoVeR running instance preloaded with AD microbiome and Sharon's datasets can be accessed via the website.

Clustering is a popular technique for data mining, knowledge discovery and visual analytics. Unfortunately, clustering interpretation can be uneasy and this difficulty can be overcome by using decision trees to explain cluster assignment. In this work, we propose an evolutionary algorithm to preprocess the data in order to obtain clusters that are simpler to interpret with decision trees. A prototype has been implemented and tested to show the benefits of the approach.