Let it flow: A static method for exploring dynamic graphs

Weiwei Cui, Xiting Wang, Shixia Liu, Nathalie H. Riche, Tara M. Madhyastha, Kwan-Liu Ma, Baining Guo

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

24 Scopus citations


Research into social network analysis has shown that graph metrics, such as degree and closeness, are often used to summarize structural changes in a dynamic graph. However there have been few visual analytics approaches that have been proposed to help analysts study graph evolutions in the context of graph metrics. In this paper, we present a novel approach, called GraphFlow, to visualize dynamic graphs. In contrast to previous approaches that provide users with an animated visualization, GraphFlow offers a static flow visualization that summarizes the graph metrics of the entire graph and its evolution over time. Our solution supports the discovery of high-level patterns that are difficult to identify in an animation or in individual static representations. In addition, GraphFlow provides users with a set of interactions to create filtered views. These views allow users to investigate why a particular pattern has occurred. We showcase the versatility of GraphFlow using two different datasets and describe how it can help users gain insights into complex dynamic graphs.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE Pacific Visualization Symposium, PacificVis 2014
PublisherIEEE Computer Society
Number of pages8
ISBN (Print)9781479928736
StatePublished - Jan 1 2014
Event2014 7th IEEE Pacific Visualization Symposium, PacificVis 2014 - Yokohama, Kanagawa, Japan
Duration: Mar 4 2014Mar 7 2014


Other2014 7th IEEE Pacific Visualization Symposium, PacificVis 2014
CityYokohama, Kanagawa


  • Dynamic Graphs
  • Flow Visualization

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software


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