dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs

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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung


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.
Titel2020 Visualization in Data Science (VDS)
ISBN (elektronisch)978-1-7281-9284-0
PublikationsstatusVeröffentlicht - 2020
VeranstaltungIEEE VIS 2020 - Virtuell, USA / Vereinigte Staaten
Dauer: 25 Okt. 202030 Okt. 2020


KonferenzIEEE VIS 2020
KurztitelVIS 2020
Land/GebietUSA / Vereinigte Staaten

Fields of Expertise

  • Information, Communication & Computing


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