Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Network visualization techniques andevaluation
Mohammad GHONIEM
The Charlotte Visualization CenterUniversity of North Carolina, Charlotte
March 15th 2007
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Outline
1 Definition and motivation of Infovis
2 Visualization of structured dataVisualization of hierarchical dataVisualization of network data
3 Visualization of dense and dynamic networksThe matrix-based representation of graphsApplication to constraint-oriented programming graphs
4 Conclusion
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Outline
1 Definition and motivation of Infovis
2 Visualization of structured dataVisualization of hierarchical dataVisualization of network data
3 Visualization of dense and dynamic networksThe matrix-based representation of graphsApplication to constraint-oriented programming graphs
4 Conclusion
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Outline
1 Definition and motivation of Infovis
2 Visualization of structured dataVisualization of hierarchical dataVisualization of network data
3 Visualization of dense and dynamic networksThe matrix-based representation of graphsApplication to constraint-oriented programming graphs
4 Conclusion
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Outline
1 Definition and motivation of Infovis
2 Visualization of structured dataVisualization of hierarchical dataVisualization of network data
3 Visualization of dense and dynamic networksThe matrix-based representation of graphsApplication to constraint-oriented programming graphs
4 Conclusion
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Information Visualization
DefinitionA compact graphical representationA graphical user interfaceFor the visualization and interaction with large numbers ofitemsthat may be a subset of an even larger dataset.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Information Visualization
Scope and usefulnessBears on data that are:
abstract, multi-dimensional, structured or unstructured.in order to:
Make discoveries, decisions, find explanations, orcommunicate about
visual patterns(trends, clusters, outliers, . . .)groups of items,individual items.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Information Visualization
Historical background
Considerable leap in hardware technology and capabilitiesProduction and storage of large volumes of data;Increased processing capabilities;Improved display capabilities.
Birth of data miningMathematical and statistical data analysis;Visual information exploration.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Information Visualization
Principles and roleMake the best use of human vision
detect patterns,sense correlations,confirm an intuition or formulate hypotheses.
Make the best use of user’s expertiseinteractive exploration,interactive construction of views.
Precedes but does not replace classical data mining.Provides robust solutions for the most frequent datastructures.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Organisation
1 Definition and motivation of Infovis
2 Visualization of structured dataVisualization of hierarchical dataVisualization of network data
3 Visualization of dense and dynamic networksThe matrix-based representation of graphsApplication to constraint-oriented programming graphs
4 Conclusion
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Tree Visualization Techniques
2D representationsNode-link diagrams (cartesian / circular)Space-filling techniques (treemaps, icicle trees, circular)Non-euclidian space (hyperbolic trees)
3D representationsNode-link diagrams (cone trees)Non-euclidian space (hyperbolic trees)
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Overview of Tree Visualizations
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Overview of Tree Visualizations
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Overview of Tree Visualizations
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Overview of Tree Visualizations
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Overview of Tree Visualizations
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Overview of Tree Visualizations
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Overview of Tree Visualizations
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Overview of Tree Visualizations
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Organisation
1 Definition and motivation of Infovis
2 Visualization of structured dataVisualization of hierarchical dataVisualization of network data
3 Visualization of dense and dynamic networksThe matrix-based representation of graphsApplication to constraint-oriented programming graphs
4 Conclusion
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Graph theory basics
DefinitionsG = (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 (v1, · · · , vk )where (vi , vi+1) ∈ E ∀i < k ,a directed path is a cycle if (vk , v1) ∈ E ,a directed graph is acyclic if it is cycle free,a graph is planar if it has an intersection free 2D drawing.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Graphs are ubiquitous
ExamplesInfrastructure networks (telecommunications, power,roads).Social networks (acquaintances, crime networks).Co-citation network, etc.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Graphs and representations
Definitiona graph is an abstract entity 6= its representations.
Layout
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Graph visualization techniques
Visualization techniquesNode-link diagrams,Adjacency matrices.
Aesthetic rules and drawing conventionsDrawing 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).
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Graph visualization techniques
Graph drawing approaches
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Graph visualization techniques
Graph drawing approaches
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Graph visualization techniques
Graph drawing approaches
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Graph visualization techniques
Graph drawing approaches
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Graph visualization overview
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Graph visualization frameworks
Existing frameworksTulip (University of Bordeaux – France),Pajek (University of Ljubljani – Slovenia),GraphViz (AT&T),JUNG (University of California, Irvine).
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Graph visualization techniques
Node-link diagramsWidespread and well studied wrt sparse graphs.Cluttered views when link density increases.Instable layout algorithmsUnusable for dynamic graphs.Begs for an alternate representation.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Monitoring co-activity graphs
Variables + Constraints
X
Y
Z
c1
c2
c3
X = 2
X = 3
Variables
X Y Z
Contraintes
c1 c2
c3
X = 2
X = 3
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Monitoring co-activity graphs
Variables + Constraints
X
Y
Z
c1
c2
c3
X = 2
X = 3
Variables
X Y Z
Contraintes
c1 c2
c3
X = 2
X = 3
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Monitoring co-activity graphs
Variables + Constraints
X
Y
Z
c1
c2
c3
X = 2
X = 3
Variables
X Y Z
Contraintes
c1 c2
c3
X = 2
X = 3
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Monitoring co-activity graphs
Variables + Constraints
X
Y
Z
c1
c2
c3
X = 2
X = 3
Variables
X Y Z
Contraintes
c1 c2
c3
X = 2
X = 3
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Monitoring co-activity graphs
Variables + Constraints
X
Y
Z
c1
c2
c3
X = 2
X = 3
Variables
X Y Z
Contraintes
c1 c2
c3
X = 2
X = 3
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Monitoring co-activity graphs
Variables + Constraints
X
Y
Z
c1
c2
c3
X = 2
X = 3
Variables
X Y Z
Contraintes
c1 c2
c3
X = 2
X = 3
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Monitoring co-activity graphs
Variables + Constraints
X
Y
Z
c1
c2
c3
X = 2
X = 3
Variables
X Y Z
Contraintes
c1 c2
c3
X = 2
X = 3
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Monitoring co-activity graphs
Variables + Constraints
X
Y
Z
c1
c2
c3
X = 2
X = 3
Variables
X Y Z
Contraintes
c1 c2
c3
X = 2
X = 3
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Co-activity graphs
The 8 queen problem
Co-activity graph
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Visualization of hierarchical dataVisualization of network data
Co-activity graphs
The 8 queen problem Co-activity graph
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Organisation
1 Definition and motivation of Infovis
2 Visualization of structured dataVisualization of hierarchical dataVisualization of network data
3 Visualization of dense and dynamic networksThe matrix-based representation of graphsApplication to constraint-oriented programming graphs
4 Conclusion
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Matrix-based representation of graphs
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Matrix-based representation of graphs
StrengthsRelies on a well-known mathematical representation.No clutter nor occlusion.Orderable, predictible for most common order relations.Displays existing and missing links.
ShortcomingsUnfamiliar visualization.Not effective for path related tasks.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Matrix-based representation of graphs
StrengthsRelies on a well-known mathematical representation.No clutter nor occlusion.Orderable, predictible for most common order relations.Displays existing and missing links.
ShortcomingsUnfamiliar visualization.Not effective for path related tasks.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Matrix-based representation of graphs
StrengthsRelies on a well-known mathematical representation.No clutter nor occlusion.Orderable, predictible for most common order relations.Displays existing and missing links.
ShortcomingsUnfamiliar visualization.Not effective for path related tasks.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
The matrix of Bertin
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Cluster revelation through permutations
Orderability magic
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Cluster revelation through permutations
Orderability magic
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Matrix-based representation of graphs
Properties of matrices
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Matrix-based representation of graphs
Multi-scale self-contained visualization
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Matrix-based representation of graphs
Multi-scale self-contained visualization
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Matrix-based representation of graphs
Multi-scale self-contained visualization
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Matrix-based representation of graphs
But...How readable is the matrix-based representation ?
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Evaluation of the readability of matrices
PrincipeCompare two alternative representations of graphs wrtA 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.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Evaluation of the readability of matrices
Taxonomy of tasksTasks related to the overview.Tasks related to vertices.Tasks related to links.Tasks related to paths.Tasks related to subgraphs.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Evaluation of the readability of matrices
Seven tasks1 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 tasksA minimal selection of tasks.Primitive tasks.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Evaluation of the readability of matrices
Experimental precautionsPreliminary demonstration and training stage.Guidelines : answer fast and right, may skip questions iftoo 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.)
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Evaluation of the readability of matrices
SetupNine 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).
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Evaluation of the readability of matrices
Highlight and selection of nodes and links
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Evaluation of the readability of matrices
User population and result analysis36 Ph.D. students or assistant professors incomputer-science.Analysis: quantitative
and qualitative + 3D model.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Evaluation of the readability of matrices
User population and result analysis36 Ph.D. students or assistant professors incomputer-science.Analysis: quantitative
and qualitative + 3D model.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Evaluation of the readability of matrices
User population and result analysis36 Ph.D. students or assistant professors incomputer-science.Analysis: quantitative and qualitative
+ 3D model.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Evaluation of the readability of matrices
User population and result analysis36 Ph.D. students or assistant professors incomputer-science.Analysis: quantitative and qualitative + 3D model.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Evaluation of the readability of matrices
ResultsBetter results with the matrix-based representation for 6/7tasks wrt
dense graphs,large graphs.
Node-link diagrams are preferable for small sparse graphs.Path related tasks are difficult to carry out.
User reactionsNot so enthusiastic in the beginning, except a few.Very positive at the end of the evaluation, except a few.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Organisation
1 Definition and motivation of Infovis
2 Visualization of structured dataVisualization of hierarchical dataVisualization of network data
3 Visualization of dense and dynamic networksThe matrix-based representation of graphsApplication to constraint-oriented programming graphs
4 Conclusion
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Visualization of constraint-oriented programminggraphs
Properties of such graphsVery densely connected.Large.Complex temporal relations between nodes.
Visualization solutionMatrix-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 solving process.Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Monitoring of dynamic graphs: example 1
Pedagogical problem (sort 100 variables)
100 variables xi∈[1,100] in [1, 100].99 constraints : ∀i ∈ [1, 99], xi < xi+1.
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Monitoring of dynamic graphs: example 1
Constraint graph of the sort problem
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Monitoring of dynamic graphs: example 2
alldiff constraints problem
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Monitoring of dynamic graphs: example 2
Automatic segmentation and animation of the solving process
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Problem structure investigations
Aid for debugging andfine-tuning of programs
3 sets of variables withstrong internal links.Problem structure isvisible at the end ofpropagation phase.Link density has nonegative impact on theclarity of the overview.
Variables × Variables
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Problem structure investigations
Aid for debugging andfine-tuning of programs
Mindom heuristicexecuted during 2minutes and interrupted.Sets #1 and #2 have astrong impact on set #3.Weak links mislead thesolver into baddecisions.
Variables × Variables
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Problem structure investigations
Aid for debugging andfine-tuning of programs
Normalized view.Initial infrequentdecisions appear indark.Poor decisions involveset #2.
Variables × Variables
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Problem structure investigations
Aid for debugging andfine-tuning of programs
Discard gradually theeffects of old decisions.The solver is trappedinto a costlyenumeration on sets #1and #3 because of a baddecision taken wrt to set#2.
Variables × Variables
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Problem structure investigations
Aid for debugging andfine-tuning of programs
Adapt the mindomheuristic.Discard constraintscausing an abnormalvolume of activity.The problem is solvedimmediately.
Variables × Variables
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Investigation of complex problem : graph coloring
Instance 4 of MycielskiVariables 11 to 21 arenot linked, obvious on amatrix.The problem can be splitinto two sub-problems.
Variables × Variables
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
The matrix-based representation of graphsApplication to constraint-oriented programming graphs
Investigation of complex problem : graph coloring
Instance 5 of MycielskiThe structure of theproblem is recurrent.An incrementalresolution strategy canbe very effective.
Variables × Variables
Mohammad GHONIEM Network visualization techniques and evaluation
Definition and motivation of InfovisVisualization of structured data
Visualization of dense and dynamic networksConclusion
Conclusion
Information visualizationThe purpose of information visualization is insight.The choice of a visualization metaphor depends on thenature of the data and the tasks at hand.Large and/or dense networks are best viewed asadjacency matrices.Dynamic graphs are best monitored as adjacencymatrices.
Mohammad GHONIEM Network visualization techniques and evaluation
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