Dynamic Network Visualization in 1.5D Lei Shi *, Chen Wang *, Zhen Wen † * IBM Research – China...
-
Upload
santiago-poser -
Category
Documents
-
view
217 -
download
2
Transcript of Dynamic Network Visualization in 1.5D Lei Shi *, Chen Wang *, Zhen Wen † * IBM Research – China...
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
central node sending/receiving trend
1-hop node
2-hop node
time-dependent edge
time-independent edge
Horizontal Glyph
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 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
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
Graph Interactions
Timeline navigation
Egocentric graph navigation
zoomzoom &
pan
drill-in to newcentral 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
22
Thank You
MerciGrazie
Gracias
Obrigado
Danke
Japanese
English
French
Russian
German
Italian
Spanish
Brazilian PortugueseArabic
Traditional Chinese
Simplified Chinese
HindiTamil
Thai
Korean