dynamic network visualization in 1.5d lei shi *, chen wang *, zhen wen † * ibm research – china...
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Dynamic Network Visualization in 1.5D
Lei Shi *, Chen Wang *, Zhen Wen †
* IBM Research – China† IBM T.J. Watson Research Center
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Mobile SMS Network – Spammer
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Mobile SMS Network – Non-Spammer
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Mobile SMS Network – Spammer/Non-Spammer
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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
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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
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Related Works: Dynamic Movie Approach
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Related Works: Small Multiple Display
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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]
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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
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Visual Metaphor
central node sending/receiving trend
1-hop node
2-hop node
time-dependent edge
time-independent edge
Horizontal Glyph
Radial Glyph
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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
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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
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Graph Interactions
Timeline navigation
Egocentric graph navigation
zoomzoom &
pan
drill-in to newcentral node view
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Case Study — Mobile SMS Network
For each people, send only one message in one time
For some people, send multiple messages in multiple times
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Case Study — Mobile SMS Network
Unidirectional communication (no reply)
Bidirectional communication (send & reply)
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Case Study — Mobile SMS Network
No communications between receivers (friends)
Connections between receivers (friends)
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Case Study — Mobile SMS Network
Smooth transmissions (the automatic scanning with powerful machine)
Irregular transmission pattern
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Case Study — Conference Author Networks Infovis author network: ego-edge mode, Prof. Stasko’s network
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Case Study — Conference Author Networks Infovis author network: network-edge mode
Dr. Wong’s network Prof. Munzner’s network
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Case Study — Conference Author Networks VAST author network
Overview Prof. Ribarsky’s network
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