dg2pix : Pixel-Based Visual Analysis of Dynamic Graphs

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Proceedings of IEEE Visualization in Data Science (VDS). Piscataway, NJ: IEEE, 2020, pp. 32-41. ISBN 978-1-72819-284-0. Available under: doi: 10.1109/VDS51726.2020.00008
Zusammenfassung

Presenting long sequences of dynamic graphs remains challenging due to the underlying large-scale and high-dimensional data. We propose dg2pix, a novel pixel-based visualization technique, to visually explore temporal and structural properties in long sequences of large-scale graphs. The approach consists of three main steps: (1) the multiscale modeling of the temporal dimension; (2) unsupervised graph embeddings to learn low-dimensional representations of the dynamic graph data; and (3) an interactive pixel-based visualization to simultaneously explore the evolving data at different temporal aggregation scales. dg2pix provides a scalable overview of a dynamic graph, supports the exploration of long sequences of high-dimensional graph data, and enables the identification and comparison of similar temporal states. We show the applicability of the technique to synthetic and real-world datasets, demonstrating that temporal patterns in dynamic graphs can be identified and interpreted over time. dg2pix contributes a suitable intermediate representation between node-link diagrams at the high detail end and matrix representations on the low detail end.

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IEEE Visualization in Data Science (VDS) (Virtual Conference), 26. Okt. 2020, Salt Lake City, Utah
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ISO 690CAKMAK, Eren, Dominik JÄCKLE, Tobias SCHRECK, Daniel A. KEIM, 2020. dg2pix : Pixel-Based Visual Analysis of Dynamic Graphs. IEEE Visualization in Data Science (VDS) (Virtual Conference). Salt Lake City, Utah, 26. Okt. 2020. In: Proceedings of IEEE Visualization in Data Science (VDS). Piscataway, NJ: IEEE, 2020, pp. 32-41. ISBN 978-1-72819-284-0. Available under: doi: 10.1109/VDS51726.2020.00008
BibTex
@inproceedings{Cakmak2020dg2pi-51036,
  year={2020},
  doi={10.1109/VDS51726.2020.00008},
  title={dg2pix : Pixel-Based Visual Analysis of Dynamic Graphs},
  isbn={978-1-72819-284-0},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={Proceedings of IEEE Visualization in Data Science (VDS)},
  pages={32--41},
  author={Cakmak, Eren and Jäckle, Dominik and Schreck, Tobias and Keim, Daniel A.}
}
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