dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationProceedings IEEE VIS Symposium on Visualization in Data Science
Number of pages10
Publication statusAccepted/In press - 2020
Event15th IEEE Conference on Visual Analytics Science and Technology: VAST 2020 - Virtual, Salt Lake City, United States
Duration: 25 Oct 202030 Oct 2020


Conference15th IEEE Conference on Visual Analytics Science and Technology
Abbreviated titleVIS 2020
CountryUnited States
CityVirtual, Salt Lake City
Internet address

Fields of Expertise

  • Information, Communication & Computing

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