research-article

Interactive Time-Series of Measures for Exploring Dynamic Networks

Authors:

James O'Donnell,

Jean-Daniel FeketeAuthors Info & Claims

AVI '20: Proceedings of the 2020 International Conference on Advanced Visual Interfaces

Article No.: 26, Pages 1 - 9

https://doi.org/10.1145/3399715.3399922

Published: 02 October 2020 Publication History

Abstract

We present MeasureFlow, an interface to visually and interactively explore dynamic networks through time-series of network measures such as link number, graph density, or node activation. When networks contain many time steps, become large and more dense, or contain high frequencies of change, traditional visualizations that focus on network topology, such as animations or small multiples, fail to provide adequate overviews and thus fail to guide the analyst towards interesting time points and periods. MeasureFlow presents a complementary approach that relies on visualizing time-series of common network measures to provide a detailed yet comprehensive overview of when changes are happening and which network measures they involve. As dynamic networks undergo changes of varying rates and characteristics, network measures provide important hints on the pace and nature of their evolution and can guide an analysts in their exploration; based on a set of interactive and signal-processing methods, MeasureFlow allows an analyst to select and navigate periods of interest in the network. We demonstrate MeasureFlow through case studies with real-world data.

References

[1]

Jae-wook Ahn, Catherine Plaisant, and Ben Shneiderman. 2013. A task taxonomy for network evolution analysis. IEEE Trans. Vis. Comput. Graph. 20, 3 (2013), 365--376. https://doi.org/10.1109/TVCG.2013.238

Digital Library

[2]

Jae-Wook Ahn, Meirav Taieb-Maimon, Awalin Sopan, Catherine Plaisant, and Ben Shneiderman. 2011. Temporal Visualization of Social Network Dynamics: Prototypes for Nation of Neighbors. In Proceedings of the 4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction (SBP'11). Springer, Berlin, Heidelberg, Germany, 309--316. https://doi.org/10.1007/978-3-642-19656-0_43

[3]

Michelle N. Arbeitman, Elleen E. M. Furlong, Farhad Imam, Eric Johnson, Brian H Null, Bruce S. Baker, Mark A. Krasnow, Matthew P. Scott, Ronald W. Davis, and Kevin P. White. 2002. Gene Expression During the Life Cycle of Drosophila melanogaster. Science 297, 5590 (2002), 2270--2275. https://doi.org/10.1126/science.1072152

[4]

Benjamin Bach. 2016. Unfolding Dynamic Networks for Visual Exploration. IEEE Comput. Graph. Appl. 36, 2 (2016), 74--82. https://doi.org/10.1109/MCG.2016.32

Digital Library

[5]

Benjamin Bach, Nathalie Henry-Riche, TimDwyer, Tara Madhyastha, Jean-Daniel Fekete, and Thomas Grabowski. 2015. Small MultiPiles: Piling time to explore temporal patterns in dynamic networks. Comput. Graph. Forum 34, 3 (2015), 31--40. https://doi.org/10.1111/cgf.12615

[6]

Benjamin Bach, Nathalie Henry Riche, Roland Fernandez, Emmanoulis Giannisakis, Bongshin Lee, and Jean-Daniel Fekete. 2015. NetworkCube: Bringing Dynamic Network Visualizations to Domain Scientists. Posters of the IEEE Visualization Conference (VIS'15). Poster.

[7]

Benjamin Bach, Emmanuel Pietriga, and Jean-Daniel Fekete. 2013. GraphDiaries: Animated Transitions and Temporal Navigation for Dynamic Networks. IEEE Trans. Vis. Comput. Graph. 20, 5 (2013), 740--754. https://doi.org/10.1109/TVCG.2013.254

Digital Library

[8]

Benjamin Bach, Conglei Shi, Nicolas Heulot, Tara Madhyastha, Tom Grabowski, and Pierre Dragicevic. 2015. Time curves: Folding time to visualize patterns of temporal evolution in data. IEEE Trans. Vis. Comput. Graph. 22, 1 (2015), 559--568. https://doi.org/10.1109/TVCG.2015.2467851

Digital Library

[9]

James P Bagrow, Erik M Bollt, Joseph D Skufca, and Daniel Ben-Avraham. 2008. Portraits of complex networks. Europhysics Letters (EPL) 81, 6 (2008), 68004. https://doi.org/10.1209/0295-5075/81/68004

[10]

Mathieu Bastian, Sebastien Heymann, and Mathieu Jacomy. 2009. Gephi: An Open Source Software for Exploring and Manipulating Networks. In Proceedings of the Third International Conference on Weblogs and Social Media (ICWSM'09). AAAI Press, Menlo Park, CA, USA, 2.

[11]

Fabian Beck, Michael Burch, Stephan Diehl, and Daniel Weiskopf. 2017. A taxonomy and survey of dynamic graph visualization. Comput. Graph. Forum 36, 1 (2017), 133--159. https://doi.org/10.1111/cgf.12791

Digital Library

[12]

Ilya Boyandin, Enrico Bertini, and Denis Lalanne. 2012. A Qualitative Study on the Exploration of Temporal Changes in Flow Maps with Animation and Small-Multiples. Comput. Graph. Forum 31, 3 (2012), 1005--1014. https://doi.org/10.1111/j.1467--8659.2012.03093.x

Digital Library

[13]

Michael Burch, Corinna Vehlow, Fabian Beck, Stephan Diehl, and Daniel Weiskopf. 2011. Parallel Edge Splatting for Scalable Dynamic Graph Visualization. IEEE Trans. Vis. Comput. Graph. 17, 12 (2011), 2344--2353. https://doi.org/10.1109/TVCG.2011.226

Digital Library

[14]

Weiwei Cui, Xiting Wang, Shixia Liu, Nathalie H Riche, Tara M Madhyastha, Kwan Liu Ma, and Baining Guo. 2014. Let it flow: a static method for exploring dynamic graphs. In Proceedings of the IEEE Pacific Visualization Symposium (Paci-ficVis'14). IEEE, Piscataway, NJ, USA, 121--128. https://doi.org/10.1109/PacificVis.2014.48

Digital Library

[15]

Pravallika Devineni, Evangelos E. Papalexakis, Danai Koutra, A. Seza Dogruöz, and Michalis Faloutsos. 2017. One Size Does Not Fit All: Profiling Personalized Time-Evolving User Behaviors. In Proceedings of the 2017 IEEE /ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'17). ACM, New York, NY, USA, 331--340. https://doi.org/10.1145/3110025.3110050

Digital Library

[16]

Nicole Dufournaud, Bernard Michon, Benjamin Bach, and Pascal Cristofoli. 2017. L'analyse des réseaux, une aide à penser: réflexions sur les stratégies économique et sociale de Marie Boucher, marchande à Nantes au XVII e siècle. In Réseaux de femmes, femmes en réseaux (XVIe-XXIe siècles). Presses Universitaires de Bordeaux, Bordeaux, France, 109--137.

[17]

Manuel Freire, Catherine Plaisant, Ben Shneiderman, and Jennifer Golbeck. 2010. ManyNets: an interface for multiple network analysis and visualization. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'10). ACM, New York, NY, USA, 213--222. https://doi.org/10.1145/1753326.1753358

Digital Library

[18]

Alexander J. Gibberd and James D. B. Nelson. 2017. Regularized Estimation of Piecewise Constant Gaussian Graphical Models: The Group-Fused Graphical Lasso. Journal of Computational and Graphical Statistics 26, 3 (2017), 623--634. https://doi.org/10.1080/10618600.2017.1302340

[19]

Mustafa Hajij, Bei Wang, Carlos Scheidegger, and Paul Rosen. 2018. Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology. In Proceedings of the IEEE Pacific Visualization Symposium (PacificVis'18). IEEE, Piscataway, NJ, USA, 125--134. https://doi.org/10.1109/PacificVis.2018.00024

[20]

Sanjay Kairam, Diana MacLean, Manolis Savva, and Jeffrey Heer. 2012. Graph-Prism: Compact Visualization of Network Structure. In Proceedings of the International Working Conference on Advanced Visual Interfaces (AVI'12). ACM, New York, NY, USA, 498--505. https://doi.org/10.1145/2254556.2254651

Digital Library

[21]

Alexandra Lee, Daniel Archambault, and Miguel Nacenta. 2019. Dynamic Network Plaid. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'19). ACM, New York, NY, USA, 14. https://doi.org/10.1145/3290605.3300360

Digital Library

[22]

Bongshin Lee, Catherine Plaisant, Cynthia Sims Parr, Jean-Daniel Fekete, and Nathalie Henry. 2006. Task Taxonomy for Graph Visualization. In Proceedings of the 2006 AVI Workshop on BEyond Time and Errors: Novel Evaluation Methods for Information Visualization (BELIV '06). ACM, New York, NY, USA, 1--5. https://doi.org/10.1145/1168149.1168168

Digital Library

[23]

James Moody, Daniel McFarland, and Skye Bender-deMoll. 2005. Dynamic network visualization. American journal of sociology 110, 4 (2005), 1206--1241. https://doi.org/10.1086/421509

[24]

Tamara Munzner. 2015. Visualization analysis and design. CRC Press, Boca Raton, FL, USA.

[25]

Adam Perer and Ben Shneiderman. 2008. Integrating Statistics and Visualization: Case Studies of Gaining Clarity during Exploratory Data Analysis. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '08). ACM, New York, NY, USA, 265--274. https://doi.org/10.1145/1357054.1357101

Digital Library

[26]

Adam Perer and Jimeng Sun. 2012. Matrixflow: temporal network visual analytics to track symptom evolution during disease progression. In AMIA annual symposium proceedings, Vol. 2012. American Medical Informatics Association, 716.

[27]

Ryan A. Rossi and Nesreen K. Ahmed. 2015. The Network Data Repository with Interactive Graph Analytics and Visualization. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15). AAAI Press, Menlo Park, CA, USA, 4292--4293.

Digital Library

[28]

Ali Saidi. 1994. Decimation-in-time-frequency FFT algorithm. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '94). IEEE, Piscataway, NJ, USA, 453--456. https://doi.org/10.1109/ICASSP.1994.389992

[29]

Ben Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proceedings of the 1996 IEEE Symposium on Visual Languages. IEEE, Piscataway, NJ, USA, 336--343. https://doi.org/10.1109/VL.1996.545307

[30]

Stef van den Elzen, Danny Holten, Jorik Blaas, and Jarke J van Wijk. 2013. Dynamic network visualization with extended massive sequence views. IEEE Trans. Vis. Comput. Graph. 20, 8 (2013), 1087--1099. https://doi.org/10.1109/TVCG.2013.263

Digital Library

[31]

Stef van den Elzen, Danny Holten, Jorik Blaas, and Jarke J. van Wijk. 2016. Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration. IEEE Trans. Vis. Comput. Graph. 22, 1 (2016), 1--10. https://doi.org/10.1109/TVCG.2015.2468078

Digital Library

[32]

Frank van Ham, Hans-Jörg Schulz, and Joan Morris DiMicco. 2009. Honeycomb: Visual Analysis of Large Scale Social Networks. In Proceedings of IFIP Conference on Human-Computer Interaction. Springer, Berlin, Heidelberg, Germany, 429--442. https://doi.org/10.1007/978-3-642-03658-3_47

Digital Library

[33]

Yong Wang, Daniel Archambault, Hammad Haleem, Torsten Moeller, Yanhong Wu, and Huamin Qu. 2019. Nonuniform Timeslicing of Dynamic Graphs Based on Visual Complexity. In Proceedings of IEEE Visualization Conference (VIS '19). IEEE, Piscataway, NJ, USA. https://doi.org/10.1109/VISUAL.2019.8933748 Short paper.

Cited By

Liu SMa RZhao CLi ZXiao JLi Q(2024)BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual AnalyticsProceedings of the ACM on Human-Computer Interaction10.1145/36373038:CSCW1(1-31)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637303
Li WSchöttler SScott-Brown JWang YChen SQu HBach B(2023)NetworkNarratives: Data Tours for Visual Network Exploration and AnalysisProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581452(1-15)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581452
Cakmak EFuchs JJackle DSchreck TBrandes UKeim D(2022)Motif-Based Visual Analysis of Dynamic Networks2022 IEEE Visualization in Data Science (VDS)10.1109/VDS57266.2022.00007(17-26)Online publication date: Oct-2022
https://doi.org/10.1109/VDS57266.2022.00007

Index Terms

Interactive Time-Series of Measures for Exploring Dynamic Networks
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Visual analytics
    2. Visualization design and evaluation methods

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AVI '20: Proceedings of the 2020 International Conference on Advanced Visual Interfaces

September 2020

613 pages

ISBN:9781450375351

DOI:10.1145/3399715

Copyright © 2020 ACM.

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Published: 02 October 2020

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AVI '20

AVI '20: International Conference on Advanced Visual Interfaces

September 28 - October 2, 2020

Salerno, Italy

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AVI '20 Paper Acceptance Rate 36 of 123 submissions, 29%;

Overall Acceptance Rate 128 of 490 submissions, 26%

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Cited By

Liu SMa RZhao CLi ZXiao JLi Q(2024)BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual AnalyticsProceedings of the ACM on Human-Computer Interaction10.1145/36373038:CSCW1(1-31)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637303
Li WSchöttler SScott-Brown JWang YChen SQu HBach B(2023)NetworkNarratives: Data Tours for Visual Network Exploration and AnalysisProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581452(1-15)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581452
Cakmak EFuchs JJackle DSchreck TBrandes UKeim D(2022)Motif-Based Visual Analysis of Dynamic Networks2022 IEEE Visualization in Data Science (VDS)10.1109/VDS57266.2022.00007(17-26)Online publication date: Oct-2022
https://doi.org/10.1109/VDS57266.2022.00007

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