1. Visualizing Large Dynamic Digraphs Michael Burch

2. Motivation Various application examples for dynamic graphs – Protein-protein interactions – Social networks – Call relations in software systems – …

3. Visualization Challenges Give an overview representation about – Vertices of a graph – Edges of a graph (adjacency edges) – Direction of the edges – Weights of the edges – Vertex hierarchy (inclusion edges) – Evolution of adjacency edges over time

4. Related Work Many dynamic graph visualization techniques exist… Have a look at our State of the art report at EuroVis 2014 Fabian Beck, Michael Burch, Stephan Diehl, Daniel Weiskopf. The state of the art in visualizing dynamic graphs. In STAR reports at EuroVis. 2014.

5. Related Work Many dynamic graph visualization techniques exist… Have a look at our State of the art report at EuroVis 2014 Webpage: http://dynamicgraphs.fbeck.com/

6. Why this work? Three novel contributions – Dynamic partial links – Splatting of partial links – Compression of splatted graphs in a sequence  Reducing the display space for the same information

7. Data Model Relational data modeled as a graph where V denotes the set of vertices and E A the directed and weighted adjacency edges

8. Data Model A dynamic weighted graph may be modeled as a function

9. Data Model A hierarchical organization of the vertices modeled as where V are the same vertices as in the graph and E I are the inclusion edges

10. Visualization Technique Time-to-Space Mapping – Benefits of time-to-space mapping for dynamic graphs Easy exploration of dynamic patterns on different levels of granularity Application of interaction techniques Mental map preservation – Drawbacks of time-to-space mapping for dynamic graphs Reduced flexibility caused by 1D layout (instead of 2D) Increased visual clutter

11. Visualization Technique Visualizing dense graphs results in visual clutter

12. Visualization Technique Edge Splatting – Weighted directed graph (adjacency edges) – Vertex hierarchy (inclusion edges)

13. Visualization Technique Edge Splatting – Transforming 2D to 1D – Bipartite graph by vertex set copy – Vertices equidistantly mapped to 1D vertical lines – Left-to-right reading direction – Vertex hierarchy attached and aligned

14. Visualization Technique Edge Splatting – Sequences of graphs mapped to sequences of narrow stripes – Vertex hierarchy displayed as layered icicle plot – Similar concept as in parallel coordinates plots

15. Visualization Technique Edge Splatting  Reduce visual clutter

16. Visualization Technique Edge Splatting without vs. with Edge Splatting

17. Visualization Technique Example: Call graphs of Junit open source software project

18. Visualization Technique Edge Splatting

19. Visualization Technique Edge Splatting

20. Partial Links

23. Case Studies Call graphs from evolving software systems – JUnit open source software project – DependencyFinder tool to compute call graph – 21 releases (call graphs) – 2,817 vertices – 15,339 edges – Hierarchical organization of the vertices

24. Junit Call Graphs Time-to-Space Mapping + Partial Links + Edge Splatting

25. Case Studies Social networks changing over time – ACM 2009 Hypertext conference – Face-to-face proximities by RFID badges – 1,178 graphs in a 3 minute time aggregation – 113 conference attendees (vertices) – 20,818 edges – Hierarchical organization by hierarchical clustering

26. Face-to-Face Contacts Time-to-Space Mapping + Partial Links + Edge Splatting

27. Visualization Technique Time-to-Space Mapping + Partial Links + Edge Splatting

28. Interaction Techniques Grid flipping (Rapid Serial Visual Presentation) Graph aggregation Graph comparison Weight filters Details-on-demand …

29. Visual Patterns Dynamic Patterns – Trends – Countertrends – Oscillations/periodicities – Temporal shifts – Anomalies and outliers

30. Discussion Visual scalability Algorithmic complexities Visual clutter and overdraw Layout dependency Comparison tasks in dynamic graphs Data attachments

31. Conclusion Visualization technique for showing dynamic graph data Time-to-space mapping + Partial links + Edge splatting Flip-book feature Identification of dynamic visual patterns Two case studies – Junit call relations – Face-to-face contacts

32. Future Work Trying more graph layouts with partial links Order of the graph vertices Conducting comparative user studies – Time-to-space mappings – Time-to-time mappings (animation) – Hybrid flip-book interaction Applying different splatting techniques Eye tracking …

33. Thank you for your attention Questions? michael.burch@visus.uni-stuttgart.de