Abstract
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 language | English |
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Title of host publication | 2020 Visualization in Data Science (VDS) |
Pages | 32-41 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-7281-9284-0 |
DOIs | |
Publication status | Published - 2020 |
Event | IEEE VIS 2020 - Virtuell, United States Duration: 25 Oct 2020 → 30 Oct 2020 http://ieeevis.org/year/2020/welcome |
Conference
Conference | IEEE VIS 2020 |
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Abbreviated title | VIS 2020 |
Country/Territory | United States |
City | Virtuell |
Period | 25/10/20 → 30/10/20 |
Internet address |
Fields of Expertise
- Information, Communication & Computing