research-article

BiSet: Semantic Edge Bundling with Biclusters for Sensemaking

Authors:

Naren RamakrishnanAuthors Info & Claims

IEEE Transactions on Visualization and Computer Graphics, Volume 22, Issue 1

Pages 310 - 319

https://doi.org/10.1109/TVCG.2015.2467813

Published: 31 January 2016 Publication History

Abstract

Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, “in-between”, to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics.

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Index Terms

BiSet: Semantic Edge Bundling with Biclusters for Sensemaking
1. Human-centered computing
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Visualization and Computer Graphics

IEEE Transactions on Visualization and Computer Graphics Volume 22, Issue 1

Jan. 2016

1034 pages

ISSN:1077-2626

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Copyright © 2015.

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IEEE Educational Activities Department

United States

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Published: 31 January 2016

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Cited By

Pan RWang ZWei YGao HOu GCao CXu JXu TChen W(2024)Towards Efficient Visual Simplification of Computational Graphs in Deep Neural NetworksIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.323083230:7(3359-3373)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TVCG.2022.3230832
Qu BZhang EZhang Y(2022)Automatic Polygon Layout for Primal-Dual Visualization of HypergraphsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311475928:1(633-642)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3114759
Sun MNamburi AKoop DZhao JLi TChung H(2022)Towards Systematic Design Considerations for Visualizing Cross-View Data RelationshipsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.310296628:12(4741-4756)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3102966
Zhao JSun MChen FChiu P(2022)Understanding Missing Links in Bipartite Networks With MissBiNIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303298428:6(2457-2469)Online publication date: 1-Jun-2022
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Wang YZhao ZLi H(2021)Overview of information visualization for business under the background of big dataProceedings of the 4th International Conference on Computer Science and Software Engineering10.1145/3494885.3494939(295-299)Online publication date: 22-Oct-2021
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Sun MShaikh AAlhoori HZhao J(2021)SightBi: Exploring Cross-View Data Relationships with BiclustersIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311480128:1(54-64)Online publication date: 24-Dec-2021
https://dl.acm.org/doi/10.1109/TVCG.2021.3114801
Wu HSun MMi PTatti NNorth CRamakrishnan N(2018)Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy ModelsACM Transactions on Knowledge Discovery from Data10.1145/304701712:1(1-34)Online publication date: 31-Jan-2018
https://dl.acm.org/doi/10.1145/3047017
Chan GXu PDai ZRen L(2018)ViBr: Visualizing Bipartite Relations at Scale with the Minimum Description Length PrincipleIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.286482625:1(321-330)Online publication date: 7-Dec-2018
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