Network visualization techniques and evaluation
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1 Network visualization techniques and evaluation The Charlotte Visualization Center University of North Carolina, Charlotte March 15th 2007
2 Outline 1 Definition and motivation of Infovis 2 3 4
3 Outline 1 Definition and motivation of Infovis 2 3 4
4 Outline 1 Definition and motivation of Infovis 2 3 4
5 Outline 1 Definition and motivation of Infovis 2 3 4
6 Information Visualization Definition A compact graphical representation A graphical user interface For the visualization and interaction with large numbers of items that may be a subset of an even larger dataset.
7 Information Visualization Scope and usefulness Bears on data that are: abstract, multi-dimensional, structured or unstructured. in order to: Make discoveries, decisions, find explanations, or communicate about visual patterns (trends, clusters, outliers,...) groups of items, individual items.
8 Information Visualization Historical background Considerable leap in hardware technology and capabilities Production and storage of large volumes of data; Increased processing capabilities; Improved display capabilities. Birth of data mining Mathematical and statistical data analysis; Visual information exploration.
9 Information Visualization Principles and role Make the best use of human vision detect patterns, sense correlations, confirm an intuition or formulate hypotheses. Make the best use of user s expertise interactive exploration, interactive construction of views. Precedes but does not replace classical data mining. Provides robust solutions for the most frequent data structures.
10 Organisation 1 Definition and motivation of Infovis 2 3 4
11 Tree Visualization Techniques 2D representations Node-link diagrams (cartesian / circular) Space-filling techniques (treemaps, icicle trees, circular) Non-euclidian space (hyperbolic trees) 3D representations Node-link diagrams (cone trees) Non-euclidian space (hyperbolic trees)
12 Overview of Tree Visualizations
13 Overview of Tree Visualizations
14 Overview of Tree Visualizations
15 Overview of Tree Visualizations
16 Overview of Tree Visualizations
17 Overview of Tree Visualizations
18 Overview of Tree Visualizations
19 Overview of Tree Visualizations
20 Organisation 1 Definition and motivation of Infovis 2 3 4
21 Graph theory basics Definitions G = (V, E), E ={(u, v)}, where (u, v) V V, two vertices are adjacent if they are connected by an edge, an edge is directed if an order is defined on its extremities, a graph is directed if its edges are directed, a directed path is a sequence of vertices (v 1,, v k ) where (v i, v i+1 ) E i < k, a directed path is a cycle if (v k, v 1 ) E, a directed graph is acyclic if it is cycle free, a graph is planar if it has an intersection free 2D drawing.
22 Graphs are ubiquitous Examples Infrastructure networks (telecommunications, power, roads). Social networks (acquaintances, crime networks). Co-citation network, etc.
23 Graphs and representations Definition Layout a graph is an abstract entity its representations.
24 Graph visualization techniques Visualization techniques Node-link diagrams, Adjacency matrices. Aesthetic rules and drawing conventions Drawing conventions: polyline drawing, grid drawing, upward/downward etc. Aesthetic rules: min. intersections, min. edge length, min. area etc. Some rules are conflicting. Some user studies and experiments (H. Purchase, C. Ware).
25 Graph visualization techniques Graph drawing approaches
26 Graph visualization techniques Graph drawing approaches
27 Graph visualization techniques Graph drawing approaches
28 Graph visualization techniques Graph drawing approaches
29 Graph visualization overview
30 Graph visualization frameworks Existing frameworks Tulip (University of Bordeaux France), Pajek (University of Ljubljani Slovenia), GraphViz (AT&T), JUNG (University of California, Irvine).
31 Graph visualization techniques Node-link diagrams Widespread and well studied wrt sparse graphs. Cluttered views when link density increases. Instable layout algorithms Unusable for dynamic graphs. Begs for an alternate representation.
32 Monitoring co-activity graphs Variables + Constraints X Y Z
33 Monitoring co-activity graphs Variables + Constraints X c 1 Y Z
34 Monitoring co-activity graphs Variables + Constraints X c 2 c 1 Y c 3 Z
35 Monitoring co-activity graphs Variables + Constraints Variables X c 2 X Y Z c 1 Y c 3 Z
36 Monitoring co-activity graphs Variables + Constraints Variables X c 2 X Y Z c 1 Y Contraintes c 3 Z c 3 c 1 c 2
37 Monitoring co-activity graphs Variables + Constraints Variables X c 2 X = 2 X Y Z c 1 Y X = 3 Contraintes c 3 c 3 Z c 1 c 2
38 Monitoring co-activity graphs Variables + Constraints Variables X c 2 X = 2 X Y Z c 1 Y X = 3 Contraintes c 3 c 3 Z c 1 c 2
39 Monitoring co-activity graphs Variables + Constraints Variables X c 2 X = 2 X Y Z c 1 Y X = 3 Contraintes c 3 X = 3 c 3 Z X = 2 c 1 c 2
40 Co-activity graphs The 8 queen problem
41 Co-activity graphs The 8 queen problem Co-activity graph
42 Organisation 1 Definition and motivation of Infovis 2 3 4
43 Matrix-based representation of graphs
44 Matrix-based representation of graphs Strengths Relies on a well-known mathematical representation. No clutter nor occlusion. Orderable, predictible for most common order relations. Displays existing and missing links. Shortcomings Unfamiliar visualization. Not effective for path related tasks.
45 Matrix-based representation of graphs Strengths Relies on a well-known mathematical representation. No clutter nor occlusion. Orderable, predictible for most common order relations. Displays existing and missing links. Shortcomings Unfamiliar visualization. Not effective for path related tasks.
46 Matrix-based representation of graphs Strengths Relies on a well-known mathematical representation. No clutter nor occlusion. Orderable, predictible for most common order relations. Displays existing and missing links. Shortcomings Unfamiliar visualization. Not effective for path related tasks.
47 Definition and motivation of Infovis The matrix of Bertin Mohammad G HONIEM
48 Cluster revelation through permutations Orderability magic
49 Cluster revelation through permutations Orderability magic
50 Matrix-based representation of graphs Properties of matrices
51 Matrix-based representation of graphs Multi-scale self-contained visualization
52 Matrix-based representation of graphs Multi-scale self-contained visualization
53 Matrix-based representation of graphs Multi-scale self-contained visualization
54 Matrix-based representation of graphs But... How readable is the matrix-based representation?
55 Evaluation of the readability of matrices Principe Compare two alternative representations of graphs wrt A predefined list of common tasks, A set of graphs of variable sizes and link density. The readability is measured according to two indicators: 1 answer time; 2 error rate.
56 Evaluation of the readability of matrices Taxonomy of tasks Tasks related to the overview. Tasks related to vertices. Tasks related to links. Tasks related to paths. Tasks related to subgraphs.
57 Evaluation of the readability of matrices Seven tasks 1 Estimate the number of vertices. 2 Estimate the number of links. 3 Find the most connected node. 4 Find a node by its name. 5 Say whether two links are connected. 6 Say whether two nodes have a common neighbor. 7 Find a path between two nodes. A few observations about the tasks A minimal selection of tasks. Primitive tasks.
58 Evaluation of the readability of matrices Experimental precautions Preliminary demonstration and training stage. Guidelines : answer fast and right, may skip questions if too difficult. Two two-fold sessions. Three breaks (5 min, 10 min, 5 min). Equalize weariness effects. Equalize learning effects. Bounded answer time (45 sec. max.)
59 Evaluation of the readability of matrices Setup Nine random graphs with increasing size and link density. Use neato/graphviz package for node-link diagrams. Matrix-based representation in OpenGL/Java. Presets tothe best advantage of each representation. Minimal interaction (node and link highlights).
60 Evaluation of the readability of matrices Highlight and selection of nodes and links
61 Evaluation of the readability of matrices User population and result analysis 36 Ph.D. students or assistant professors in computer-science. Analysis: quantitative
62 Evaluation of the readability of matrices User population and result analysis 36 Ph.D. students or assistant professors in computer-science. Analysis: quantitative
63 Evaluation of the readability of matrices User population and result analysis 36 Ph.D. students or assistant professors in computer-science. Analysis: quantitative and qualitative
64 Evaluation of the readability of matrices User population and result analysis 36 Ph.D. students or assistant professors in computer-science. Analysis: quantitative and qualitative + 3D model.
65 Evaluation of the readability of matrices Results Better results with the matrix-based representation for 6/7 tasks wrt dense graphs, large graphs. Node-link diagrams are preferable for small sparse graphs. Path related tasks are difficult to carry out. User reactions Not so enthusiastic in the beginning, except a few. Very positive at the end of the evaluation, except a few.
66 Organisation 1 Definition and motivation of Infovis 2 3 4
67 Visualization of constraint-oriented programming graphs Properties of such graphs Very densely connected. Large. Complex temporal relations between nodes. Visualization solution Matrix-based representation: no clutter, space-filling technique, adapted for focus + context techniques (fisheye), multi-scale representation (clustering + agregation). History animation through dynamic queries, Automatic segmentation of the Network solving visualization process. techniques and evaluation
68 Monitoring of dynamic graphs: example 1 Pedagogical problem (sort 100 variables) 100 variables x i [1,100] in [1, 100]. 99 constraints : i [1, 99], x i < x i+1.
69 Monitoring of dynamic graphs: example 1 Constraint graph of the sort problem
70 Monitoring of dynamic graphs: example 2 alldiff constraints problem
71 Monitoring of dynamic graphs: example 2 Automatic segmentation and animation of the solving process
72 Problem structure investigations Aid for debugging and fine-tuning of programs 3 sets of variables with strong internal links. Problem structure is visible at the end of propagation phase. Link density has no negative impact on the clarity of the overview. Variables Variables
73 Problem structure investigations Aid for debugging and fine-tuning of programs Mindom heuristic executed during 2 minutes and interrupted. Sets #1 and #2 have a strong impact on set #3. Weak links mislead the solver into bad decisions. Variables Variables
74 Problem structure investigations Aid for debugging and fine-tuning of programs Normalized view. Initial infrequent decisions appear in dark. Poor decisions involve set #2. Variables Variables
75 Problem structure investigations Aid for debugging and fine-tuning of programs Discard gradually the effects of old decisions. The solver is trapped into a costly enumeration on sets #1 and #3 because of a bad decision taken wrt to set #2. Variables Variables
76 Problem structure investigations Aid for debugging and fine-tuning of programs Adapt the mindom heuristic. Discard constraints causing an abnormal volume of activity. The problem is solved immediately. Variables Variables
77 Investigation of complex problem : graph coloring Instance 4 of Mycielski Variables 11 to 21 are not linked, obvious on a matrix. The problem can be split into two sub-problems. Variables Variables
78 Investigation of complex problem : graph coloring Instance 5 of Mycielski The structure of the problem is recurrent. An incremental resolution strategy can be very effective. Variables Variables
79 Information visualization The purpose of information visualization is insight. The choice of a visualization metaphor depends on the nature of the data and the tasks at hand. Large and/or dense networks are best viewed as adjacency matrices. Dynamic graphs are best monitored as adjacency matrices.
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