Institute of Software, Chinese Academy of Sciences Hierarchical Focus+Context Heterogeneous Network...
-
Upload
osborne-mcdonald -
Category
Documents
-
view
212 -
download
0
Transcript of Institute of Software, Chinese Academy of Sciences Hierarchical Focus+Context Heterogeneous Network...
Institute of Software, Chinese Academy of Sciences
Hierarchical Focus+Context Heterogeneous Network Visualization
Lei ShiJoint work with Qi Liao, Hanghang Tong, Yifan Hu, Yue Zhao and
Chuang Lin
State Key Lab of Computer ScienceInstitute of Software
Chinese Academy of Sciences
Institute of Software, Chinese Academy of SciencesNetwork Visualization
SocialNetworks
Population Migration Networks
TextNetworks
Gene Networks
Institute of Software, Chinese Academy of Sciences
Very few has been done on Heterogeneous
Network!
Institute of Software, Chinese Academy of SciencesOutline
Heterogeneous Network: Our Definition
Problem
Related Work
Methodology Overview
Summarization Algorithms and Performance
Visual Design and Interaction
Case Study
Conclusion
Institute of Software, Chinese Academy of SciencesHeterogeneous Network
中国政府客户场景举例Networks Heterogeneous Networks
Graphs Attributed Graphs
For InfoVis
Institute of Software, Chinese Academy of SciencesAttributed Graph
中国政府客户场景举例Imagine this as a teacher-student graph
Node Type
Teacher
Student
Class (Math, Chinese…)
Exp. (Senior, Junior…)
Position (Prof., AP…)
Gender (M/F)
Grading (100, 90+…)
Degree (PhD, MS…)
Node Attribute+Graph Topology += Heterogeneous Network
Institute of Software, Chinese Academy of SciencesProblem
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?
Institute of Software, Chinese Academy of SciencesRelated Works - Semantic
PivotGraph[Wattenberg’06]
One attribute:
Two attribute:
Institute of Software, Chinese Academy of SciencesRelated Works - Semantic
Dimensionselection
Institute of Software, Chinese Academy of SciencesRelated Works - Semantic
OntoVis [Shen et al. ’06]
Ontology Graph
Institute of Software, Chinese Academy of SciencesRelated Works - Topology
Cluster-based network visualization [Quigley’00][Auber’03][Abello’06][Shi’09]
Institute of Software, Chinese Academy of SciencesRelated Works - Topology
Compression-based network visualization [Dunne‘13][Dwyer’13][Shi’13]
Institute of Software, Chinese Academy of SciencesRelated 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.
Institute of Software, Chinese Academy of SciencesMotivation
Can we combine them?(Semantic-based network visualization &
Topology-based network visualization)
Why to combine?Semantic-based: high compression rate but coarse-grainedTopology-based: low compression rate and too fine-grained
Institute of Software, Chinese Academy of SciencesFramework
Hierarchical Focus+Context Approach
Semantic:
Semantic+Topology:
Topology
Video Demo: 1:10 ~ 3:58
Institute of Software, Chinese Academy of SciencesSummarization Algorithms
Level 1/2: Semantic Aggregation (SA)
Allow multiple node type/attributes
Institute of Software, Chinese Academy of SciencesSummarization Algorithms
Level 4: Strong Structural Equivalence (SSE)
Mostly topology-based summarization
Institute of Software, Chinese Academy of SciencesSummarization Algorithms
Level 3: Regular Equivalence
Too many regular equivalence possibilities…
Institute of Software, Chinese Academy of SciencesSummarization Algorithms
Level 3: Relative Regular Equivalence (RRE)
Regular equivalence over semantic aggregationA simplification of Regular Interior
Institute of Software, Chinese Academy of SciencesSummarization Algorithms
Structural EquivalenceSemantic Aggregation
Relative Regular Equivalence
Allow fuzzy equivalence through k-mean
Institute of Software, Chinese Academy of SciencesPerformance
Semantic Aggregation
Structural EquivalenceRelative Regular Equivalence
Institute of Software, Chinese Academy of SciencesVisual Design: OnionGraph
By node type
By node attribute
By RRE
Data legend
Onions
Institute of Software, Chinese Academy of SciencesInteractions
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)
Institute of Software, Chinese Academy of SciencesCase Study
HUA communication network visual analytics
Institute of Software, Chinese Academy of SciencesConclusion & 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
Institute of Software, Chinese Academy of Sciences
Thank you !
This work is supported in part by NSFC grant 61379088, China National 973 project 2014CB340301, NSF grant IIS1017415, U.S. Army Research Laboratory Cooperative Agreement W911NF-09-2-0053 and DARPA project W911NF-12-C-0028.
We thank anonymous reviewers for their instructive comments and suggestions that help to shape this work!