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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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Presentation on theme: "Dynamic Network Visualization in 1.5D Lei Shi *, Chen Wang *, Zhen Wen † * IBM Research – China † IBM T.J. Watson Research Center."— Presentation transcript:

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

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

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 zoom & pan 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 OverviewProf. Ribarsky’s network

22 22 Thank You Merci Grazie Gracias Obrigado Danke Japanese English French Russian German Italian Spanish Brazilian Portuguese Arabic Traditional Chinese Simplified Chinese Hindi Tamil Thai Korean


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