Dynamic Network Visualization
in 1.5D
Lei Shi *, Chen Wang *, Zhen Wen †
* IBM Research – China
† IBM T.J. Watson Research Center
Mobile SMS Network – Spammer
Mobile SMS Network – Non-Spammer
Mobile SMS Network – Spammer/Non-Spammer
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
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
Related Works: Dynamic Movie Approach
Related Works: Small Multiple Display
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]
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
Visual Metaphor
Horizontal Glyph
2-hop node
central node
sending/receiving trend
1-hop node
time-dependent
edge
time-independent
edge
Radial Glyph
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 timedependent 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
Graph Layout
 Customized force-directed layout model
for small/medium-sized networks:
– Split the central trend node into several subnodes
– 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 timeindependent edge forces
 Circular graph layout for large networks
– Partition
– Sort
– Assign
Graph Interactions
 Timeline navigation
zoom &
pan
zoom
 Egocentric graph navigation
drill-in to new
central node view
Case Study — Mobile SMS Network
 For each people, send only one message in one time
For some people, send multiple messages in multiple times
Case Study — Mobile SMS Network
 Unidirectional communication (no reply)
Bidirectional communication (send & reply)
Case Study — Mobile SMS Network
 No communications between receivers (friends)
Connections between receivers (friends)
Case Study — Mobile SMS Network
 Smooth transmissions (the automatic scanning with powerful machine)
Irregular transmission pattern
Case Study — Conference Author Networks
 Infovis author network: ego-edge mode, Prof. Stasko’s network
Case Study — Conference Author Networks
 Infovis author network: network-edge mode
Dr. Wong’s network
Prof. Munzner’s network
Case Study — Conference Author Networks
 VAST author network
Overview
Prof. Ribarsky’s network
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