Dual Adjacency Matrix: Exploring Link Groups in Dense Networks

Dinkla, Kasper; Riche, Nathalie Henry; Westenberg, Michel A.

doi:10.1111/cgf.12643

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…matrix have been introduced before [13][14][15], BioMOBS is unique since it is specifically customized for the exploration of multi-omics data (S3 Fig) in biomolecular disease pathways and integrated in a disease contextualization workflow. The combination of interactive visualisation options in MOBS can be seen as a collection of the most pertinent elements of the current state of the art in network visualisation, tailored into a systems biology analysis tool.…”

Section: Plos Onementioning

confidence: 99%

BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation

Heylen,

Peeters,

Aerts

et al. 2023

PLoS ONE

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One of the challenges in multi-omics data analysis for precision medicine is the efficient exploration of undiscovered molecular interactions in disease processes. We present BioMOBS, a workflow consisting of two data visualization tools integrated with an open-source molecular information database to perform clinically relevant analyses (https://github.com/driesheylen123/BioMOBS). We performed exploratory pathway analysis with BioMOBS and demonstrate its ability to generate relevant molecular hypotheses, by reproducing recent findings in type 2 diabetes UK biobank data. The central visualisation tool, where data-driven and literature-based findings can be integrated, is available within the github link as well. BioMOBS is a workflow that leverages information from multiple data-driven interactive analyses and visually integrates it with established pathway knowledge. The demonstrated use cases place trust in the usage of BioMOBS as a procedure to offer clinically relevant insights in disease pathway analyses on various types of omics data.

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Section: Plos Onementioning

confidence: 99%

BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation

Heylen,

Peeters,

Aerts

et al. 2023

PLoS ONE

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“…Grouping nodes and links is an efficient method to provide high-level structures of entity relationships. Dinkla et al [41] introduce Dual Adjacency Matrix (DAM), a link-centric matrix visualization. DAM supports users to do link-centric tasks such as finding common nodes shared by a given link group and finding link groups that share nodes.…”

Section: Event Correlation / Causality Analysis (A2)mentioning

confidence: 99%

PDViz: A Visual Analytics Approach for State Policy Data

Han

Nayeem

Windett

et al. 2023

Computer Graphics Forum

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Sub-national governments across the United States implement a variety of policies to address large societal problems and needs. Many policies are picked up or adopted in other states. This process is called policy diffusion and allows researchers to analyse and compare the social, political, and contextual characteristics that lead to adopting certain policies, as well as the efficacy of these policies once adopted. In this paper, we introduce PDViz, a visual analytics approach that allows social scientists to dynamically analyse the policy diffusion history and underlying patterns. It is designed for analysing and answering a list of research questions and tasks posed by social scientists in prior work. To evaluate our system, we present two usage scenarios and conduct interviews with domain experts in political science. The interviews highlight that PDViz provides the result of policy diffusion patterns that align with their domain knowledge as well as the potential to be a learning tool for students to understand the concept of policy diffusion.

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“…We identified two embedded visualization techniques, one that shows the graph as a node-link diagram [PvW08] and one that shows the graph as a matrix representation [DHRW15]. Pretorius and van Wijk [PvW08] visualize edge groups as nodes positioned in the center of the visualization.…”

Section: Edge Group Structure Visualizationsmentioning

confidence: 99%

“…The edges of the graph are partitioned by letting every link pass through the node that represents its group (Figure g). The Dual Adjacency Matrix [DHRW15] visualizes the edge groups integrated into the graph visualization (Figure h). The rows (columns) of the top left quadrant represent edge groups.…”

Section: Edge Group Structure Visualizationsmentioning

confidence: 99%

Visualizing Group Structures in Graphs: A Survey

Vehlow

Beck

Weiskopf

2016

Computer Graphics Forum

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Graph visualizations encode relationships between objects. Abstracting the objects into group structures provides an overview of the data. Groups can be disjoint or overlapping, and might be organized hierarchically. However, the underlying graph still needs to be represented for analyzing the data in more depth. This work surveys research in visualizing group structures as part of graph diagrams. A particular focus is the explicit visual encoding of groups, rather than only using graph layout to indicate groups implicitly. We introduce a taxonomy of visualization techniques structuring the field into four main categories: visual node attributes vary properties of the node representation to encode the grouping, juxtaposed approaches use two separate visualizations, superimposed techniques work with two aligned visual layers, and embedded visualizations tightly integrate group and graph representation. The derived taxonomies for group structure and visualization types are also applied to group visualizations of edges. We survey group-only, group-node, group-edge and group-network tasks that are described in the literature as use cases of group visualizations. We discuss results from evaluations of existing visualization techniques as well as main areas of application. Finally, we report future challenges based on interviews we conducted with leading researchers of the field.

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Dual Adjacency Matrix: Exploring Link Groups in Dense Networks

Cited by 5 publications

References 31 publications

BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation

BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation

PDViz: A Visual Analytics Approach for State Policy Data

Visualizing Group Structures in Graphs: A Survey

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