dg2pix : Pixel-Based Visual Analysis of Dynamic Graphs

Cakmak, Eren; Jäckle, Dominik; Schreck, Tobias; Keim, Daniel A.

dg2pix : Pixel-Based Visual Analysis of Dynamic Graphs

Dateien

Cakmak_2-2cjsu6eu0tpp9.pdfGröße: 10 MBDownloads: 190

Datum

2020

Angaben zur Forschungsförderung

European Union (EU): 830892

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen 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

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.

Fachgebiet (DDC)

004 Informatik

Konferenz

IEEE Visualization in Data Science (VDS) (Virtual Conference), 26. Okt. 2020, Salt Lake City, Utah

Zitieren

ISO 690

CAKMAK, 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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Universitätsbibliographie

Ja