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

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