visualizing large dynamic digraphs michael burch

Visualizing Large Dynamic Digraphs

Michael Burch

Motivation

• Various application examples for dynamic graphs– Protein-protein interactions

– Social networks

– Call relations in software systems

– …

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

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.

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/

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

Data Model

• Relational data modeled as a graph

where V denotes the set of vertices and EA the directed

and weighted adjacency edges

Data Model

• A dynamic weighted graph may be modeled as a function

Data Model

• A hierarchical organization of the vertices modeled as

where V are the same vertices as in the graph and EI are

the inclusion edges

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

Visualization Technique

• Visualizing dense graphs results in visual clutter

Visualization Technique

• Edge Splatting– Weighted directed graph (adjacency edges)– Vertex hierarchy (inclusion edges)

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

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

Visualization Technique

• Edge Splatting Reduce visual clutter

Visualization Technique

• Edge Splatting

without vs. with Edge Splatting

Visualization Technique

• Example: Call graphs of Junit open source software project

Visualization Technique

• Edge Splatting

Visualization Technique

• Edge Splatting

Partial Links

Partial Links

Partial Links

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

Junit Call Graphs

• Time-to-Space Mapping + Partial Links + Edge Splatting

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

Face-to-Face Contacts

• Time-to-Space Mapping + Partial Links + Edge Splatting

Visualization Technique

• Time-to-Space Mapping + Partial Links + Edge Splatting

Interaction Techniques

• Grid flipping (Rapid Serial Visual Presentation)

• Graph aggregation• Graph comparison• Weight filters • Details-on-demand• …

Visual Patterns

• Dynamic Patterns– Trends

– Countertrends

– Oscillations/periodicities

– Temporal shifts

– Anomalies and outliers

Discussion

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

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

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• …

Thank you for your attention

Questions? michael.burch@visus.uni-stuttgart.de