1 Sunbelt, 2/18/05 Interactive Visualizations to Explore Dynamic Network Data Jim Blythe USC Info Sciences Institute Cathleen McGrath Loyola Marymount

2 Sunbelt, 2/18/05 Visualizing changing network data Much network data concerns relations that change over time, or with another variable But current visualization tools focus on animations  Little help visualizing the trend of a change over time (the big picture)  Little help visualizing the details of a moment of change (drill down) We need more sophisticated tools for longitudinal data More flexible visualizations, linked to analysis, tested Handle processes on networks, multivariate dependencies, …

3 Sunbelt, 2/18/05 Contributions of our recent work in KrackPlot 1. Layout techniques for longitudinal data 2. Summary streams (help with the big picture) 3. Animation between network snapshots (drill down) 4. Difference graphs (drill down)

4 Sunbelt, 2/18/05 1. Layout techniques for longitudinal network data We want to lay out the network over time so that motion highlights “interesting” changes BUT successive independent layouts of network snapshots often have high ‘motion noise’  Irrelevant motion is confusing  Too little motion leads to poor snapshot layouts  Node motion as implicit proxy for structural change …

5 Sunbelt, 2/18/05 Our approach for successive layouts Use previous layout as initial at each time point (c.f. chain anchor of Moody et al.), with two additions: 1. Layout using a version of annealing that forces small increments of change 2. Find optimal rotation to align successive layouts More efficient than laying out whole timeline as a layered network Better continuity that simple chain-anchoring See demo using Newcomb data

6 Sunbelt, 2/18/05 Comparing frames with and without alignment

7 Sunbelt, 2/18/05 2. Summary streams It is hard to see the big picture (to keep track of patterns of change over time) from animations alone Our approach: Use slider to help navigate in visualization Use area below slider to graph features of networks or nodes over time Now the slider helps relate interesting points in time to the related network change KrackPlot dynamically talks to R for analytical packages

8 Sunbelt, 2/18/05 Example of summary streams

9 Sunbelt, 2/18/05 3. Animating structural changes Node motion easily visible BUT is really a proxy for higher-dimensional structural network change.  harder for viewers to catch the links that change between snapshots Our approach: trying alternative ways to animate link changes along with node motion.  e.g. fading, link growing,..  Techniques from graph drawing to highlight concerted motion  Need user data to evaluate impact.

10 Sunbelt, 2/18/05 Snapshots illustrating structural changes?

11 Sunbelt, 2/18/05 4. Difference graphs Problem: Hard to track multiple changes over one time instant. Animations don’t give the user time to notice changes Approach: Create a static representation of the change between the two networks that can be inspected at leisure.  Can use color to show change in links, or thickness in valued links  Can place the difference graph in the animation stream between successive time points. Users can stop the animation or move to the midpoint to view the change.

12 Sunbelt, 2/18/05

13 Sunbelt, 2/18/05 Ongoing and Future work Integrate with temporal smoothing techniques for visualizing processes on graphs User studies to measure impact and best form for summary streams, difference graphs, animations More data sources, e.g. UN voting data with Paulette Lloyd. (See our talk on Sunday!) Want to try it?

14 Sunbelt, 2/18/05 BACKUP SLIDES

15 Sunbelt, 2/18/05 Layout techniques for longitudinal network data Problem: successive independent layouts of network snapshots often have high ‘motion noise’ Solutions  Lay out successive snapshots, with previous layout as initial (Moody et al: ‘chain anchor’)  Lay out as one layered, time-connected network (Brandes & Corman)  Our current approach in KP: chain anchor with forced-short annealing + alignment optimization

16 Sunbelt, 2/18/05 Standard layout applied to whole, layered data set Each snapshot is a separate layer of the graph Link the same node in adjacent snapshots Advantages Uses any existing layout approach Adjustable influence of layers on each other, through link strength Reduces disproportionate influence of early time points Disadvantage Too expensive for large networks

17 Sunbelt, 2/18/05 Newcomb fraternity data Each member provides friendship rankings for all other members, each of 15 time periods I show only the top 3 ranked friends for each member, treated equally

18 Sunbelt, 2/18/05 Related work Moody/MacFarland: SONIA visualization tool for longitudinal data. Lays out networks with chain-anchored spring embeddings. Mrvar: Using Pajek for longitudinal data. Efficient temporal model with intervals attached to nodes and links. Brandes & Corman: Lay out temporal data as one layered network. Also used by Kobourov. Friedrich & Eades: Find the best affine transformation (rotate, translate, resize) to show change with rigid network motion. Havre et al.: ThemeRiver shows change in topics in news stream over time.

19 Sunbelt, 2/18/05 Outline Layout techniques for longitudinal data Summary streams (big picture) Animation between network snapshots (drill down) Difference graphs (drill down) Related and future work

20 Sunbelt, 2/18/05 Summary streams Problem: Hard to keep track of patterns of change over time from animations Approach:  Use slider to help navigate in visualization  Use area below slider to graph features of networks or nodes over time  The slider then connects interesting points in time with the networks  Communicates with SNA package in R for analysis help