1. Hierarchical Focus+Context Heterogeneous Network Visualization
Lei Shi
Joint work with Qi Liao, Hanghang Tong, Yifan Hu, Yue Zhao and Chuang Lin
State Key Lab of Computer Science
Institute of Software
Chinese Academy of Sciences

2. Network Visualization
Population Migration Networks
Social
Networks
Gene Networks
Text
Networks

3. Very few has been done on Heterogeneous Network!

4. Outline Heterogeneous Network: Our Definition Problem Related Work
Methodology Overview
Summarization Algorithms and Performance
Visual Design and Interaction
Case Study
Conclusion

5. Heterogeneous Network
Networks
Heterogeneous Networks
中国政府客户场景举例
For
InfoVis
Graphs
Attributed Graphs

6. teacher-student graph
Attributed Graph
中国政府客户场景举例
Imagine this as a
teacher-student graph
Class (Math, Chinese…)
Exp. (Senior, Junior…)
Teacher
Position (Prof., AP…)
Gender (M/F)
Student
Grading (100, 90+…)
Degree (PhD, MS…) + +
Graph Topology
Node Type
Node Attribute
= Heterogeneous Network

7. Problem How to visualize heterogeneous networks (attribute graphs) ?
中国政府客户场景举例
How to visualize
heterogeneous networks (attribute graphs) ?
On large graphs
(102~ 106+ nodes)
How to summarize
heterogeneous networks?
For visual analysis
How to navigate
the abstraction of heterogeneous networks?

8. Related Works - Semantic
PivotGraph
[Wattenberg’06]
One attribute:
Two attribute:

9. Related Works - Semantic
Dimension
selection

10. Related Works - Semantic
OntoVis [Shen et al. ’06]
Ontology Graph

11. Related Works - Topology
Cluster-based network visualization [Quigley’00][Auber’03][Abello’06][Shi’09]

12. Related Works - Topology
Compression-based network visualization [Dunne‘13][Dwyer’13][Shi’13]

13. Related Works - Others
B. Shneiderman and A. Aris. Network visualization by semantic substrates. IEEE Transactions on Visualization and Computer Graphics, 12(5):733–740, 2006.
A. Bezerianos, F. Chevalier, P. Dragicevic, N. Elmqvist, and J. D. Fekete. GraphDice: A system for exploring multivariate social networks. Computer Graphics Forum, 29(3):863–872, 2010.
N. Cao, J. Sun, Y.-R. Lin, D. Gotz, S. Liu, and H. Qu. FacetAtlas: Multifaceted visualization for rich text corpora. IEEE Transactions on Visualization and Computer Graphics, 16(6):1172–1181, 2010.
H. Kang, C. Plaisant, B. Lee, and B. B. Bederson. NetLens: Iterative exploration of content-actor network data. Information Visualization, 6(1):18–31, 2007.
B. Lee, G. Smith, G. Robertson, M. Czerwinski, and D. S. Tan. FacetLens: exposing trends and relationships to support sensemaking within faceted datasets. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1293–1302, 2009.
C. Dunne, N. H. Riche, B. Lee, R. Metoyer, and G. Robertson. GraphTrail: analyzing large multivariate, heterogeneous networks while supporting exploration history. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1663–1672, 2012.
E. Gansner, Y. Koren, and S. North. Topological fisheye views for visualizing large graphs. In IEEE Symposium on Information Visualization (InfoVis’04), 2004.

14. Can we combine them? Why to combine? Motivation
(Semantic-based network visualization &
Topology-based network visualization)
Why to combine?
Semantic-based: high compression rate but coarse-grained
Topology-based: low compression rate and too fine-grained

15. Hierarchical Focus+Context Approach
Framework
Hierarchical Focus+Context Approach
Semantic:
Semantic+
Topology:
Topology
Video Demo: 1:10 ~ 3:58

16. Summarization Algorithms
Level 1/2: Semantic Aggregation (SA)
Allow multiple node type/attributes

17. Summarization Algorithms
Level 4: Strong Structural Equivalence (SSE)
Mostly topology-based summarization

18. Summarization Algorithms
Level 3: Regular Equivalence
Too many regular equivalence possibilities…

19. Summarization Algorithms
Level 3: Relative Regular Equivalence (RRE)
Regular equivalence over semantic aggregation
A simplification of Regular Interior

20. Summarization Algorithms
Semantic Aggregation
Structural Equivalence
Relative Regular Equivalence
Allow fuzzy equivalence through k-mean

21. Performance Semantic Aggregation Relative Regular Equivalence
Structural Equivalence

22. Visual Design: OnionGraph
By node attribute
Onions
By RRE
By node type
Data legend

23. Interactions
Network navigation: Hierarchical focus+context (v.s. hierarchical traversal)
Multiple focuses: Through abstraction profiling
Network filters: Global node filtering (v.s. local filtering)
Neighborhood charting: Show the distribution of neighborhood attributes (roles)

24. Case Study
HUA communication network visual analytics

25. Conclusion & Contribution
We present OnionGraph: the “first” system that allow networks to be aggregated, visualized and navigated based on both topology and node semantics
We propose a non-trivial hierarchical approach, including a suite of node clustering algorithms, the focus+context interaction and the global filtering operations
We design the “onion” metaphor to represent both the node aggregations and their hierarchy and attribute information

26. Thank you !
This work is supported in part by NSFC grant , China National 973 project 2014CB340301, NSF grant IIS , U.S. Army Research Laboratory Cooperative Agreement W911NF and DARPA project W911NF-12-C-0028.
We thank anonymous reviewers for their instructive comments and suggestions that help to shape this work!