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

Network Visualization Population Migration Networks Social Networks Gene Networks Text Networks

Very few has been done on Heterogeneous Network!

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

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

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

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?

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

Related Works - Semantic Dimension selection

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

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

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

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

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

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

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

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

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

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

Performance Semantic Aggregation Relative Regular Equivalence Structural Equivalence

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

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)

Case Study HUA communication network visual analytics

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

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!