Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

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

Publikation: Beitrag in einer FachzeitschriftArtikel

Abstract

The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively generate temporal summaries to abstract overlapping sequences of graphs into compact snapshots. Second, we apply graph embeddings to the snapshots to learn low-dimensional representations of each sequence of graphs to speed up specific analytical tasks (e.g., similarity search). Third, we visualize the evolving data from a coarse to fine-granular snapshots to semi-automatically analyze temporal states, trends, and outliers. The approach enables us to discover similar temporal summaries (e.g., reoccurring states), reduces the temporal data to speed up automatic analysis, and to explore both structural and temporal properties of a dynamic graph. We demonstrate the usefulness of our approach by a quantitative evaluation and the application to a real-world dataset.
Originalspracheenglisch
Aufsatznummer9222072
Seiten (von - bis)517-527
Seitenumfang11
FachzeitschriftIEEE Transactions on Visualization and Computer Graphics
Jahrgang27
Ausgabenummer2
Frühes Online-DatumOkt 2020
DOIs
PublikationsstatusVeröffentlicht - Feb 2021
Veranstaltung15th IEEE Conference on Visual Analytics Science and Technology: VAST 2020 - Virtual, Salt Lake City, USA / Vereinigte Staaten
Dauer: 25 Okt 202030 Okt 2020
http://ieeevis.org/year/2020/welcome

ASJC Scopus subject areas

  • Software
  • !!Signal Processing
  • !!Computer Vision and Pattern Recognition
  • !!Computer Graphics and Computer-Aided Design

Fields of Expertise

  • Information, Communication & Computing

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