VAST 2020 Contest Challenge: GraphMatchMaker—Visual Analytics for Graph Comparison and Matching

Tovanich, Natkamon; Pister, Alexis; Richer, Gaëlle; Valdivia, Paola; Prieur, Christophe; Fekete, Jean-Daniel; Isenberg, Petra

doi:10.1109/mcg.2021.3091955

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Tovanich et al. [TPR*21] propose a visual analytics tool to compare multivariate, sometimes bipartite, dynamic graphs and find common structures. Yet, their tool does not handle roles and is designed for the specific task of matching a sub‐graph into a large graph.…”

Section: Related Workmentioning

confidence: 99%

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

Pister

Prieur

Fekete

2023

Computer Graphics Forum

Self Cite

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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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Section: Related Workmentioning

confidence: 99%

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

Pister

Prieur

Fekete

2023

Computer Graphics Forum

Self Cite

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“…Large structured datasets are often represented as graphs, and numerous efforts target effective large-graph visualisations [21], [22]. Many of these efforts have been triggered by the field of Visual Analytics, which employs analytical reasoning facilitated by interactive visual interfaces [23], [24].…”

Section: B Visualising and Comprehending Large Graphs And Modelsmentioning

confidence: 99%

Model Sensemaking Strategies: Exploiting Meta-Model Patterns to Understand Large Models

Martínez-Lasaca,

Díez,

Guerra

et al. 2023

2023 ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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The increasing popularity of model-based and lowcode platforms has raised the need to understand large models -especially in industrial settings. However, current approaches mainly rely on graph-based visual metaphors, which do not scale well with large model sizes. To address this issue, we introduce model sensemaking strategies: purposeful model visualisations based on alternative visual metaphors. We define them as reusable patterns that yield tailored visualisations when applied to meta-models. This paper presents a catalogue of domain-specific and domain-agnostic sensemaking strategies, and a recommender that suggests suitable strategies for a given meta-model. To showcase the framework's applicability, we have implemented some of these strategies in Dandelion, an industrial, low-code graphical language workbench for the cloud. Using this platform, we have evaluated the effectiveness of the strategies to visualise large industrial models by the UGROUND company.

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VAST 2020 Contest Challenge: GraphMatchMaker—Visual Analytics for Graph Comparison and Matching

Cited by 2 publications

References 12 publications

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

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

Model Sensemaking Strategies: Exploiting Meta-Model Patterns to Understand Large Models

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