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ClusterVis: visualizing nodes attributes in multivariate graphs

Authors:

Carla M. Dal Sasso Freitas,

Marco WincklerAuthors Info & Claims

SAC '17: Proceedings of the Symposium on Applied Computing

Pages 174 - 179

https://doi.org/10.1145/3019612.3019684

Published: 03 April 2017 Publication History

Abstract

Many computing applications imply dealing with network data, for example, social networks, communications and computing networks, epidemiological networks, among others. These applications are usually based on multivariate graphs, i.e., graphs in which nodes and edges have multiple attributes. Most of the visualization techniques described in the literature for dealing with multivariate graphs focus either on problems associated with the visualization of topology or on problems associated with the visualization of the nodes' attributes. The integration of these two components (topology and multiple attributes) in a single visualization is a challenge due to the necessity of simultaneously representing the connections and attributes, possibly generating overlapping elements. Among usual strategies to overcome this legibility problem we find filtering and aggregation, which make possible a simplified representation providing a general view with reduced size and lower density. However, this simplification may lead to a reduction of the amount of information being displayed, while in several applications the graph details still need to be represented in order to allow in-depth data analysis. In face of that, we propose ClusterVis, a visualization technique aiming at exploring nodes attributes pertaining to sub-graphs, which are either obtained from clustering algorithms or some user-defined criteria. The technique allows comparing attributes of nodes while keeping the representation of the relationships among them. The technique was implemented within a visualization framework and evaluated by potential users.

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Cited By

Azevedo BBranco PCunha F(2023)Design for Science: Proposing an Interactive Circular 2-Level AlgorithmCreating Digitally10.1007/978-3-031-31360-8_16(445-472)Online publication date: 3-Dec-2023
https://doi.org/10.1007/978-3-031-31360-8_16
Menin AMaillot PFaron CCorby OFreitas CGandon FWinckler M(2023)LDViz: A Tool to Assist the Multidimensional Exploration of SPARQL EndpointsWeb Information Systems and Technologies10.1007/978-3-031-24197-0_9(149-173)Online publication date: 18-Jan-2023
https://doi.org/10.1007/978-3-031-24197-0_9
Wang SChen SCai TWu Y(2022)MULTI-NETVIS: Visual Analytics for Multivariate NetworkApplied Sciences10.3390/app1217840512:17(8405)Online publication date: 23-Aug-2022
https://doi.org/10.3390/app12178405
Show More Cited By

Index Terms

ClusterVis: visualizing nodes attributes in multivariate graphs
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Information visualization

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Published In

cover image ACM Conferences

SAC '17: Proceedings of the Symposium on Applied Computing

April 2017

2004 pages

ISBN:9781450344869

DOI:10.1145/3019612

Conference Chair:
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Maria Lencastre
University of Pernambuco, Brazil

Copyright © 2017 ACM.

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

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Published: 03 April 2017

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CNPq
CAPES

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SAC 2017

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SIGAPP

SAC 2017: Symposium on Applied Computing

April 3 - 7, 2017

Marrakech, Morocco

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Citations

Cited By

Azevedo BBranco PCunha F(2023)Design for Science: Proposing an Interactive Circular 2-Level AlgorithmCreating Digitally10.1007/978-3-031-31360-8_16(445-472)Online publication date: 3-Dec-2023
https://doi.org/10.1007/978-3-031-31360-8_16
Menin AMaillot PFaron CCorby OFreitas CGandon FWinckler M(2023)LDViz: A Tool to Assist the Multidimensional Exploration of SPARQL EndpointsWeb Information Systems and Technologies10.1007/978-3-031-24197-0_9(149-173)Online publication date: 18-Jan-2023
https://doi.org/10.1007/978-3-031-24197-0_9
Wang SChen SCai TWu Y(2022)MULTI-NETVIS: Visual Analytics for Multivariate NetworkApplied Sciences10.3390/app1217840512:17(8405)Online publication date: 23-Aug-2022
https://doi.org/10.3390/app12178405
Floricel CNipu NBiggs MWentzel ACanahuate GVan Dijk LMohamed AFuller CMarai G(2022)THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer TherapyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311481028:1(151-161)Online publication date: Jan-2022
https://doi.org/10.1109/TVCG.2021.3114810
Menin ADo MDal Sasso Freitas CCorby OFaron CGiboin AWinckler M(2022)Using Chained Views and Follow-Up Queries to Assist the Visual Exploration of the Web of Big Linked DataInternational Journal of Human–Computer Interaction10.1080/10447318.2022.211252940:2(224-240)Online publication date: 23-Aug-2022
https://doi.org/10.1080/10447318.2022.2112529
DeBellis MDutta B(2022)Semantic Data Science in the COVID‐19 PandemicData Science with Semantic Technologies10.1002/9781119865339.ch15(393-426)Online publication date: 25-Oct-2022
https://doi.org/10.1002/9781119865339.ch15
Menin AMichel FGandon FGazzotti RCabrio ECorby OGiboin AMarro SMayer TVillata SWinckler M(2021)Covid-on-the-Web: Exploring the COVID-19 scientific literature through visualization of linked data from entity and argument miningQuantitative Science Studies10.1162/qss_a_001642:4(1301-1323)Online publication date: 1-Dec-2021
https://doi.org/10.1162/qss_a_00164
Menin ACava RFreitas CCorby OWinckler M(2021)Towards a Visual Approach for Representing Analytical Provenance in Exploration Processes2021 25th International Conference Information Visualisation (IV)10.1109/IV53921.2021.00014(21-28)Online publication date: Jul-2021
https://doi.org/10.1109/IV53921.2021.00014
Vogogias AArchambault DBach BKennedy J(2020)Visual Encodings for Networks with Multiple Edge TypesProceedings of the 2020 International Conference on Advanced Visual Interfaces10.1145/3399715.3399827(1-9)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3399715.3399827
Wentzel ACanahuate Gvan Dijk LMohamed AFuller CMarai G(2020)Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology2020 IEEE Visualization Conference (VIS)10.1109/VIS47514.2020.00063(281-285)Online publication date: Oct-2020
https://doi.org/10.1109/VIS47514.2020.00063
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