1. Visualizing Network Data Richard A. Becker Stephen G.Eick Allan R.Wilks

2. Network Data Understanding network data is crucial Dataset Internet data, telephone data… www, e-mail communication… Challenge: coping with data volumes

3. SeeNet Goal: visualizing the network data, not just the structure of the network itself Reduce the amount of data Aggregation, Averaging, Thresholding, Exception reporting Techniques: Static displays, Interactive controls, Animation

4. Network Displays Linkmaps Map clutter problem Nodemaps Use aggregation Omit detailed information Matrix Display In/out nodes are assigned in rows/cols Omit geographical information

5. Linkmaps vs. Nodemaps

6. Parameter Focusing Select the parameter values controlling the characteristics of the display Parameter Values Statistic, Levels, Geography/Topology, Time, Aggregation, Size, Color.. Issues Large space of possible values Most combinations are not understandable Displays are sensitive to particular values

7. Interactive Control Identification Linkmap/Nodemaps Sliders for line length, thickness, animation speed, color, time, symbol size Matrix Display Permutation of the rows/cols(drag-and-drop) Zooming and Bird’s-Eye Animation/Conditioning/Sound

8. Interactive Control(link length)

9. Further Examples CICNet Packet-switched data network Schematic, not geographic E-mail Communications Nodes(users) and Links(#emails) World Internet Display statistics from the internet

10. Further Examples(2) CICNet E-mail Communication

11. My Favorite Sentences To solve the display clutter problem, we invented a suite of parametric techniques embodied in a dynamic graphics software system called SeeNet that enable a user to focus the display and thereby reveal patterns in the network data

12. Contribution Focus on both a network and data on that network Try to solve the display clutter problem Dynamic parameter control

13. Reference Fairchild, Poltrock, Furnas[15] the SemNet system Sarkar, Brown[16] Visualizing the structure of sparse networks Paulish[17] Edge concentration, gradual focusing Ahlberg, Shneiderman[34] A nearly instantaneous response is critical

14. Critique Strength Three different display methods Lots of parameters that users can choose Easy to manipulate parameters Can produce good visualization for various network datasets Weakness Is it easy to find the best combination of parameters and display method?

15. What has happened to this topic? Constructing Interactive Network Visual Interfaces (1998) Constructing Interactive Network Visual Interfaces (1998) Cited by CyberNet: A framework for managing networks using 3D metaphoric worlds CyberNet: A framework for managing networks using 3D metaphoric worlds Real-Time Geographic Visualization of World Wide Web Traffic (1996) Real-Time Geographic Visualization of World Wide Web Traffic (1996) CAIDA visualization tools http://www.caida.org/tools/visualization/