How Human Brain Understand Visualisation and Graph Visualisation Evaluation

1. How Human Brain Understand Visualisation and Graph Visualisation Evaluation Kai Xu National ICT Australia Sydney, Australia

2. Part I:How Human Braid Understand Visualisation

3. Most of the content can be found in the book “Information Visualization – Perception for Design”. It’s author Colin Ware at University of New Hampshire is a close friend of the information visualisation research group at School of IT. Acknowledgement

4. Table of Contents • The human visual perception system. • Visual Attention and pre-attentive patterns.

5. Visual Perception System Information flow Visualisation Eyes Brain

6. Visual Perception System Brain processing speed Task complexity

7. Visual Perception System Stage 1 • Rapid parallel processing: billions of neurons; • Extraction of orientation, texture, color, and motion features.

8. Visual Perception System Stage 2 • Slower processing than stage 1; • Detection of 2D patterns, contours and regions.

9. Visual Perception System Stage 3 • Slow serial processing; • Involve both working and long-term memory; • Object identification and eye-hand coordination.

10. Visual Angle • Visual angle: the angle subtended by an object at the eye of an observer. • A thumbnail held at arm length subtends about 1 degree of visual angle.

11. Visual field of view when gazing straight ahead. Can see slightly more than 180 degrees horizontally; Much less angle vertically. The irregular boundary of left and right fields are caused by facial features such as nose. The dark grey area is the region of binocular overlap. Human Visual Field

12. Visual Acuity • If focus on the central point, every character is about equally distinct. • This is because the visual acuity decreases quickly with the distance from fovea.

13. Acuity test Acuity distribution Line test Points test

14. Pixels and Brain Pixels Brain pixel screen pixel 1 bp Small Screen Big Screen

15. Ultimate Display

16. Visual Attention and Pre-Attentive Patterns • In the stage 1 of perception system, the whole visual field is processed in parallel and very fast; • The information that can be captured in this stage are easily distinguished. • Pre-attentive patterns (pop-out effects). • Should be considered when designing visualisation. • Some examples.

17. Color

18. Orientation

19. Motion

20. Size

21. Simple shading

22. Conjunction (does not pop out)

23. Compound features (do not pop out)

24. Surrounded colors do not pop out

25. Laws of pre attentive display • Must stand out on some simple dimension • color, • simple shape = orientation, size • motion, • depth • Conjunctions of pre-attentive dimensions do not always work.

26. Lessons for Information Visualisation • Can be used for individual symbols or areas; • Avoid possible negative effect: • Do not use large areas of strong color. • Orthogonality: use a different channel for a different type of information. • Example: Mapping high dimensional data to display variables. • Position (2) • Orientation (1) • Size (1) • Motion (2)++ • Blinking (1) • Color (3) • …

27. Part II: Graph Visualisation Evaluation

28. Table of Contents • Quick review • Introduction • Evaluation of graph drawing aesthetics • Evaluation of graph layout methods • Evaluation of large graph visualization • Conclusions

29. Review - Drawing conventions

30. Review - Graph Layout • Important part of graph visualization • Finds “good” positions for nodes and edges • To improve “graph readability”, i.e., facilitate people's understanding of the graph structure • Review: Layout algorithm covered in the previous lectures: • Tree layout • Layered layout (Sugiyama method) • Force-directed layout (spring algorithm)

31. Layered drawing nodes are placed on horizontal layers Radial drawing the layers are mapped to concentric circles HV drawing places the edges horizontally or vertically Space-filling methods (Treemap) Inclusion indicates parent-child relationship; Improved space efficiency Tree

32. Layered drawing (framework) Cycle removal: if there is directed cycles, temporarily reverses the direction of some to make the graph acyclic; Layer assignment: nodes are assigned to horizontal layers, and thus determines their y-coordinate; Crossing reduction: within each layer the nodes are ordered to reduce the number of crossings; Horizontal coordinate assignment: the x-coordinates of each vertex is determined. Directed Graph

33. Force-directed methods A graph is treated as a system of entities with force acting between them. The algorithm seeks a configuration with locally minimal energy, i.e. , a position for every entity such that the sum of the forces on each entity is zero. Common example Spring embedder Undirected Graph

34. Introduction • What’s covered so far: • Various Graph layout algorithms • This lecture: how these affect people's understanding of the graph. • Are they effective at all? • Which one is relatively more effective? • Also: visualization of large graphs • Where the traditional aesthetics and layout algorithms do not really work

35. Aesthetics are the graphic properties layout algorithm try to optimise. Crossings: Minimization of the total crossing number Area Minimization of drawing area Only meaningful to some layout. Example, grid drawing with integer coordinates Aspect ratio The ratio of the long and short edge length of its covering rectangle Ideal case is to obtain any aspect ratio in a given range (so the drawing can fit into differently shaped screen space) Graph Drawing Aesthetics

36. Graph Drawing Aesthetics • Edge length (several variations): • minimization of the sum of the edge length; • minimization of the maximum edge length; • minimization of the variance of the edge length; • only meaningful to some layout algorithm. • Bends (several variations): • minimization of the total number of bends; • minimization of maximum number of bends on an edge; • minimization of the variance of the number of bends on the edge; • trivially satisfied by straight-line drawing.

37. Graph Drawing Aesthetics • Angular resolution: • maximization of the smallest angle; • especially relevant for straight-line drawing. • Symmetry: • display the symmetries of the graph in drawing • reflective and rotational symmetry • Orthogonality: • how well the edges are parallel to the axes, and how well the nodes match to a grid; • Upward flow: • for directed graph only, • how well edges are pointing to a specified direction (usually upward);

38. Evaluation • Measuring the performance of subjects (users) completing certain task(s). • Graph-related tasks are used for graph visualization. • Example: find the shortest path between 2 nodes in a graph. • Performance is usually measured by • Accuracy • Completion time

39. Graph-Related Task Performance There are many factors affect the performance • The difficulty level of the task • Simple: find all the neighbors of a node • Hard: find all the nodes have graph distance 2 to two given nodes. • Size of the graphs • Small • Large • Subjects background • Whether they are familiar with graph visualization or not • Whether they are familiar with certain application domain (for domain-specific tasks). • And many more … • Should consider/control as many as possible when doing a test.

40. Table of Contents • Introduction • Evaluation of graph drawing aesthetics • Evaluation of graph layout methods • Evaluation of large graph visualization • Conclusions

41. Do Aesthetics Affect Graph Readability ? • Problem: Most aesthetics are proposed based on experience • Later becomes something the research community agree on • Study: readability of abstract graph • Tasks are not domain specific Purchase, H.C. et al. (1996)

42. Three aesthetics are tested: Minimizing edge crossings, Minimizing bends, and Showing symmetry. Two planar graphs are used one with 16 nodes and 18 edges the other with 16 nodes and 28 edges Nine drawings are produced for each graph, with three levels (few, some and many) of bends, crossings, and symmetry respectively. To isolate the effect of each aesthetic, the drawings with different bend levels shows no crossings or symmetry. the same for the two other aesthetics (manual layout). Dataset

43. Tasks: • Shortest path: the length of the shortest path between two nodes; • Connections between nodes: minimum number of nodes needs to be removed to disconnect two nodes; • Connections between subgraphs: minimum number of edges needs to be removed to disconnect two subgraphs. • Results • Effective: increasing bends or crossings decreases readability; • Not clear: symmetry.

44. Caveats • Dataset is fairly simple • Small planar graph • The selected tasks are similar • All focus on path between nodes or subgraphs, • this hardly cover all the information a graph structure can possibly convey. • It is possible that change in the dataset and/or tasks can alter the results

45. Which Aesthetic is the most important? • The relative importance among aesthetics • Including 5 aesthetics: • minimizing edge crossings, • minimizing bends, • symmetry. • minimum angle • orthogonality Purchase, H.C (1997)

46. Dataset (Similar to last work) • Planar graph with 16 nodes and 28 edges is used • 5 aesthetics and 10 drawings • 2 for each aesthetics: representing a strong or weak presence. • b: bends, c: crossings, m: minimal angle, o: Orthogonality, s: symmetry

47. Tasks (the same as the last work) • Shortest path: between two nodes; • Connections between nodes: number of nodes to disconnect two nodes; • Connections between subgraphs: number of nodes to disconnect two subgraphs. • Results • Most important: reducing the number of crossing; • Less effective: minimizing the number of bends and maximizing symmetry; • Not obvious: maximizing the minimum angle and orthogonality.

48. Does Aesthetics Affect Cognitive Load? • From a cognitive psychology angle • Testing aesthetics that affect shortest path task performance: • Continuity (path bendiness): the angular deviation from a straight line. • Number of crossings and average crossing angles: the crossings on the shortest path, and the angle of crossing. • Number of branches: the number of edges connect to the nodes on the shortest path but not part of the path. • Shortest path length and total edge length Ware, C. et al. (2002)

49. Task • find the shortest path between 2 given nodes • Dataset • 180 drawings with per-defined parameters. • 42 nodes in each graph, • 2 examples

50. Results: • Important: path continuity. • Edge crossings • Neutral: the total number of edge crossings in the graph. • Important: those cross the shortest path. • Important: the number of branches emanating from nodes on the path.