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!