1. Dynamic Network Visualization in 1.5D
Lei Shi *, Chen Wang *, Zhen Wen †
* IBM Research – China
† IBM T.J. Watson Research Center

2. Mobile SMS Network – Spammer

3. Mobile SMS Network – Non-Spammer

4. Mobile SMS Network – Spammer/Non-Spammer

5. Outline Problem Related Works & Previous Solutions Data Processing
Dynamic Ego Network
Event-based Dynamic Networks
Visualization
Metaphor
Graph layouts
Interactions
Case Study
Mobile SMS Networks
Infovis/VAST Conferences

6. Background & Research Problem
Dynamic networks are overwhelming in the reality, big value add-on with visualization
Demonstrate huge evolving social network over SNS/Twitter for community detection
Show the dynamically changing ad-hoc-routing sensor networks for diagnosis purpose
Visual evidence of growing telecom networks for role identification: employee retention
Problem with dynamic network visualization
How to encode the time dimension
3D? Video? Summarization?
How to deal with scalability
Finer time granularity => Larger network complexity => (visual clutter, bigger computation cost)
Usability for interactive analytics
Help automate pattern discovery
Advanced text trend visualization
Generic theme definitions: topics, words, categories, classifications
Content representations: single word/phrase/sentence -> multiple list of keywords summarizing content of different time segments
Comprehensive text data model and vis-data mapping
Cover most text data corpus compared to related works
Flexible representation of text data facets
Effective applications to multiple domains with the unified approach
Leverage interactions for insight finding
Integrated visual text analytics system with online data processing, retrieval and visualization
Scalable to large size and incremental scenario
Easy support of different applications only by replacing the data index
Generic visualization appliance for dynamic textual data
Support textual data with only time facet up to many facets

7. Related Works: Dynamic Movie Approach

8. Related Works: Small Multiple Display

9. Related Works: Dynamic Graph Drawing
Objective: preserve the user’s mental map [ELM91][MEL95]
Orthogonal ordering
Proximity relationships
Topology
Mental-map preserving dynamic graph drawing algorithms
Online dynamic graph drawing algorithms: compute the layout of one time frame only from its previous time frame and the graph change
Graph adjustment, e.g. force-scan algorithm [MEL95]
Extension from KK model [BBP07]
Incremental graph layout [North95][DKM06]
Offline dynamic graph drawing algorithms: take all the graphs in previous time frame into consideration
Optimize global stability [DGK01][CKN03]
Encode the graph change in multi-layer representation [BC02]
Special graph/drawing types
Hierarchical graph [North95][NW02], clustered graph [HEW98][FT04]
Orthogonal graph [PT98][GBP04], radial graph [YFD01]

10. 1.5D Dynamic Network Visualization
Basic idea: only consider the dynamic ego network central to one node
Many network analytics applications are egocentric: person role analysis, company collaborations analysis
Rationality: demultiplex the data in network domain (1.5D Vis) v.s. time domain (movie approach) v.s. space domain (small multiple displays)
Benefits:
Fit both time and network info into a single static 2D visualization (0.5D time, 1.5D network)
Reduced network size and layout computation complexity, less visual clutter
Better support dynamic network analytics, e.g. temporal network pattern discovery
Trade-offs:
Will lose the overall graph topology semantics and the topology evolving patterns
Compensate a little with interactions

11. Visual Metaphor Horizontal Glyph 2-hop node
central node sending/receiving trend
Radial Glyph
1-hop node
time-dependent edge
time-independent edge

12. Data Processing for 1.5D Visualization
3 steps to generate the dynamic ego network data for 1.5D visualization
Slotting:
Extraction: reduce each slotted graph into the ego graph central to the selected node
Compression: aggregate the ego graphs into a single graph with time- dependent and time-independent edges
Event-based dynamic networks
Insertion: the new event node is added to the graph, an edge is added between the event node and existing nodes if this event ever happens to it at a specific time

13. Graph Layout
Customized force-directed layout model for small/medium-sized networks:
Split the central trend node into several sub- nodes
Fix the sub-node locations at Y axis
Add stability constraints to non-central nodes to place them near their average time to the center
A balance of time-dependent and time- independent edge forces
Circular graph layout for large networks
Partition
Sort
Assign

14. Graph Interactions Timeline navigation Egocentric graph navigation
zoom &
pan
zoom
drill-in to new
central node view

15. Case Study — Mobile SMS Network
For each people, send only one message in one time
For some people, send multiple messages in multiple times

16. Case Study — Mobile SMS Network
Unidirectional communication (no reply)
Bidirectional communication (send & reply)

17. Case Study — Mobile SMS Network
No communications between receivers (friends)
Connections between receivers (friends)

18. Case Study — Mobile SMS Network
Smooth transmissions (the automatic scanning with powerful machine)
Irregular transmission pattern

19. Case Study — Conference Author Networks
Infovis author network: ego-edge mode, Prof. Stasko’s network

20. Case Study — Conference Author Networks
Infovis author network: network-edge mode
Dr. Wong’s network
Prof. Munzner’s network

21. Case Study — Conference Author Networks
VAST author network
Overview
Prof. Ribarsky’s network

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