VERTIGo: A Visual Platform for Querying and Exploring Large Multilayer Networks

Cuenca, Erick; Sallaberry, Arnaud; Ienco, Dino; Poncelet, Pascal

doi:10.1109/tvcg.2021.3067820

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…Visualization tools depend mainly on the size of our networks ( Summer et al, 2015 ; Zou et al, 2017). Currently, multilayer network visualization is limited to small networks and requires a consequent pre-formatting of the data ( Smith-Aguilar et al, 2019 ; Hammoud and Kramer, 2020 ; Sebestyén, Domokos and Abonyi, 2020 ).The study of multilayer networks based on the PPI network is constantly evolving and will become more powerful with advancement of more powerful mathematical models offering better predictions ( Kapadia et al, 2019 ; Karatzas et al, 2021 ; Cuenca et al, 2022 ). Different perspectives on the integration of PPI networks can be imagined.…”

Section: Discussionmentioning

confidence: 99%

“…Each network interacts with the other networks. The different monoplex networks will form distinct layers which will be connected by the inter-side nodes ( Rappoport and Shamir, 2018 ; Yan et al, 2018 ; Zoppi et al, 2021; Cuenca et al, 2022 ). Recently Arena3dweb ( Karatzas et al, 2021 ), a web application incorporating these algorithms and offering a visualization of multi-layer graphs in a 3D space, has enabled GPCR signaling pathways implicated in melanoma.…”

Section: Methods Based On Text Miningmentioning

confidence: 99%

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Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin

Bodein

Scott‐Boyer

et al. 2022

Front. Mol. Biosci.

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At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

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Section: Discussionmentioning

confidence: 99%

Section: Methods Based On Text Miningmentioning

confidence: 99%

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin

Bodein

Scott‐Boyer

et al. 2022

Front. Mol. Biosci.

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“…To lower the complexity barrier, several visual graph query systems have been developed to allow analysts to rapidly build and refine their queries visually. GRAPHITE [CFT*08] and VERTIGo [CSIP21] allow specifying a graph query as a node‐link diagram that the user creates interactively. Shadoan and Weaver [SW13] use a similar concept with hypergraphs to filter multi‐dimensional data.…”

Section: Related Workmentioning

confidence: 99%

ComBiNet: Visual Query and Comparison of Bipartite Multivariate Dynamic Social Networks

Pister

Prieur

Fekete

2023

Computer Graphics Forum

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We present ComBiNet, a visualization, query, and comparison system for exploring bipartite multivariate dynamic social networks. Historians and sociologists study social networks constructed from textual sources mentioning events related to people, such as marriage acts, birth certificates and contracts. We model this type of data using bipartite multivariate dynamic networks to maintain a representation faithful to the original sources while not too complex. Relying on this data model, ComBiNet allows exploring networks using both visual and textual queries using the Cypher language, the two being synchronized to specify queries using the most suitable modality; simple queries are easy to express visually and can be refined textually when they become complex. These queries are used for applying topological and attribute‐based selection on the network. Query results are visualized in the context of the whole network and over a geographical map for geolocalized entities. We also present the design of our interaction techniques for querying social networks to visually compare the selections in terms of topology, measures and attribute distributions. We validate the query and comparison systems by showing how they have been used to answer historical questions and by explaining how they have been improved through a usability study conducted with historians.

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“…Visual query interfaces have been used in many application areas and allow users to specify queries in an intuitive way [Ege97,CWW*10,MA18]. Cuenca et al [CSIP22] propose Vertigo, an approach to construct and suggest graph queries and explore their results in multi-layer networks. Vertigo visualizes all detected subgraphs as an overlayed heatmap to a graph drawing.…”

Section: Related Workmentioning

confidence: 99%

Vimo: Visual Analysis of Neuronal Connectivity Motifs

Troidl

Warchol

Choi

et al. 2022

Preprint

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Recent advances in high-resolution connectomics provide researchers access to accurate reconstructions of vast neuronal circuits and brain networks for the first time. Neuroscientists anticipate analyzing these networks to gain a better understanding of information processing in the brain. In particular, scientists are interested in identifying specific network motifs, i.e., repeating subgraphs of the larger brain network that are believed to be neuronal building blocks. To analyze these motifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to the detailed 3D reconstructions of the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains in large brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings of the involved neurons and synapses. To reduce visual clutter and simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by continuous visual abstractions that allows the user to transition from the highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure and synaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly to form a longer synaptic chain. We evaluate Vimo in a user study with seven domain experts and an in-depth case study on motifs in the central complex (CX) of the fruit fly brain.

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VERTIGo: A Visual Platform for Querying and Exploring Large Multilayer Networks

Cited by 8 publications

References 29 publications

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

ComBiNet: Visual Query and Comparison of Bipartite Multivariate Dynamic Social Networks

Vimo: Visual Analysis of Neuronal Connectivity Motifs

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