1. Graph Visualization and Beyond … Anne Denton, April 4, 2003 Including material from a paper by Ivan Herman, Guy Melançon, and M. Scott Marshall

2. Outline Graph Visualization Part 1 discussed Graph drawing and graph visualization Graph layout Graph Visualization Part 2 Navigation of large graphs Visualization of Node Data Glyphs New idea: combine both Graph Visualization Part 2 continued Reorganization of data: Clustering

3. Navigation and Interaction Zoom and pan (discussed previously) Geometric zooming Semantic zooming Clustering Fisheye Distortion Incremental Exploration and Navigation

4. Focus + Context Techniques Zooming looses contextual information Focus + context keeps context Example Fisheye distortion

5. Fisheye Distortion Process Pick focus point Map points within radius using a concave monotonic function Example: Sarkar-Brown distortion function

6. Problem with Fisheye Distortion should also be applied to links Prohibitively slow (polyline) Alternative Continue using lines Can result in unintended line crossings Other Alternative Combine layout with focus+context  Hyperbolic viewer  Other combinations possible (e.g. balloon view with focus-dependent radii) but not yet done

7. Incremental Exploration and Navigation For very large graphs (e.g. Internet) Small portion displayed Other parts displayed as needed Displayed graph small  Layout and interaction times may be small Example not from the paper http://touchgraph.sourceforge.net/ (Force-directed? Note how animation helps adjusting to new layout)

8. Visualization of Node Data?? So far mostly connectivity Exceptions: Size of files in fly-over Color represented stock performance in http://www.smartmoney.com/marketmap Common for data in a spatial context Glyphs like weather map symbols Tufte has many more suggestions

9. Weather Map Symbols Well-known from newspaper weather maps Interestingly: hard to find on the web!? Example below encodes 7 items of information in the symbol 4 of them graphical 2 coordinates by its position on the map

10. Chernoff’s Faces Assumption: Humans are good at processing facial features

11. Star-Plot Different directions correspond to different properties Example: 12 chemical properities Measured on 53 mineral samples (Hand, Mannila, Smyth, “Principles of Data Mining”, MIT Press 2001)

12. Idea Glyphs for node data Connectivity through any of the graph visualization tools Example: 5 properties of yeast genes / proteins for arms 1 property for color

13. Explanation of Node Information

14. Example Nodes “Important” gene Essential Close to center of chromosome Much known Relatively long (not involved in AHR pathway) Pseudo gene I.e. no real gene “change” gene short

15. Clustering Structure-based clustering Most common in graph visualization Often retain structure of graph  Useful for user orientation Content-based clustering Application specific Can be used for Filtering: de-emphasis or removal of elements from view Search: emphasis of an element or group of elements

16. Clustering continued Common goal Finding disjoint clusters Clumping Finding overlapping clusters Common technique Least number of edges between neighbors (Ratio Cut technique in VLSI design)

17. Hierarchical Clustering From successive application of clustering process Can be navigated as tree

18. Visualization of higher levels Herman et al. say glyphs are used (?) P. Eades, Q. Feng, “Multilevel Visualization of Clustered Graphs, ” Lecture Notes in Computer Science”, 1190, pp 101-112, 1997

19. Node Metrics Measure abstract feature Give ranking Edge metrics also possible Structure-based or content-based Examples Application-specific weight Degree of the node “Degree of Interest” (Furnas)

20. Methods of representing unselected nodes Ghosting De-emphasizing or relegating nodes to background Hiding Not displaying at all Grouping Grouping under super -node representation

21. Summary Part 1 showed Graph drawing and graph visualization Overlap but different goals and problems Graph layout: Much is known from graph drawing Part 2 Navigation of large graphs Key tool in dealing with size Reorganization of data: Clustering Still much to be done New Research Combine graph visualization with glyph techniques for node data