Exploring Social Networks with Matrix-Based Representations Nathalie Henry* & Jean-Daniel Fekete IN|SITU / AVIZ Lab. INRIA / Laboratoire de Recherche en Informatique *Université de Sydney
[email protected],
[email protected]
The problem Using Node-Link diagrams to visualize: • Tree-like • Small-world • Almost-complete http://www.infovis-wiki.net/index.php/Social_Network_Generation June 3, 2007
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What social scientists are looking for • What are the communities? • How actors are linked within the community? • How communities are linked? • Who is central?
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Proposing a readable representation for dense graphs • What are the communities? • How actors are linked within the community? • How communities are linked? • Who is central?
?
[Ghoniem et al. 05] June 3, 2007
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A
A C
Matrix Visualization
A
X X X
B
X
C B
D
B C D
X
D
? ?
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Matrix vs NodeLink • •
+
Î Readable for dense graphs
• •
Fast navigation Fast manipulation
• • • • •
Intuitive Compact More readable for path following More effective for small graphs More effective for sparse graphs
• •
Useless without layout Node overlapping Edge crossing
Î Usable interactively
•
-
Usable without reordering No node overlapping No edge crossing
• • •
More readable for some tasks
Less intuitive Use more space Weak for path following tasks
Î Not readable for dense graphs
• June 3, 2007
Manipulation requires layout computation
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Communicate
Explore
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Participatory Design • What Social Science researchers – – – –
Use? (representations, software) Analyze? (datasets) Do? (tasks, exploration process) Want? (aspiration)
Observation Evaluation
Brainstorming Prototyping http://insitu.lri.fr/~nhenry/Workshop.html
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Needs expressed for an exploratory analysis system • Multiple representations • Interaction… instead of parameter tuning [Henry&Fekete06] • • • • • • •
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Overviews Connected Components Representation Global Information on Graph and Social Networks – Data, Attributes, SNA: actors, relationship, degree distribution, diameter, 5 most connected, 5 less connected, centrality measures. Multiples représentations: Nœuds-liens (moreno30’s), Matrices (forsyth40’s) Layout for node-link, ordering for matrices Interactions directly on the network – Filtering, Clustering (multiples), Aggregation Compare, Confront, Annotate
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Possible solutions 1. Improve one representation
¾ Better layout/ordering
2. Combine both representations ¾ MatrixExplorer 3. Augment one representation
¾ MatLink
4. Find hybrid representations
¾ NodeTrix
Find other representations
¾ TreePlus, Links over Treemap, NetLens, Semantic Substrates…
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1. Improve one representation Layout (Node-Link) Order (Matrix)
Reorder to understand Bertin, 1967
• Why?
• Survey in progress – Interactive techniques – Algorithms for reordering tables – Algorithms for graphs linearization
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Identifying Visual Patterns
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2. Combine both representations MatrixExplorer
MatrixExplorer [Henry&Fekete06]
• Matrices to explore • Node-Link diagrams to present findings June 3, 2007
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3. Augment one representation MatLink
MatLink[Henry&Fekete07] • Solving the pathrelated tasks problem for matrices • Augmenting matrices with interactive links
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MatLink: significantly improving matrices •
Controlled experiment – 3 vis. x 6 datasets x 5 tasks Matrix , Node-Link, MatLink Data: From almost-trees To complete-graphs Including small-world networks Tasks:
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1. CommonNeighbour, 2. ShortestPath, 3. MostConnected, 4. ArticulationPoint, 5. LargestClique
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4. Find a hybrid representation NodeTrix
NodeTrix[Henry et al.07] • Designed for smallworld networks – Globally sparse – Locally dense
• Visualizing dense subgraphs as matrices • Interact to create, edit and remove the matrices June 3, 2007
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NodeTrix
VIDEO : http://insitu.lri.fr/~nhenry/nodetrix/nodetr ix.mov
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NodeTrix: the NetVis Nirvana? 9 Can you see every node? 9 Can you count each node’s degree? 9 Can follow every link from its source to its destination? 9 Can you idenfity clusters and outliers?
• Node Labels • Link Labels (excentric labels?!) • … even clusters labels • Node Attributes • Link Attributes • … even clusters attributes • Directed Graph (links width?!) … But… It’s gonna be crowded here !
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Visual Patterns
Cross Pattern
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Block Pattern
Mixte Pattern
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Visual Patterns
Infovis Coauthorship (133 actors)
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Using Interaction for Story-telling
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Future Directions • Scaling up to very large network... …the problem of reordering
• Provide usable tools to sociologists... …the problem of bug fixing
• Navigating and aggregating [Zame] • Towards collaborative exploration • From exploration to story telling
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La Fin
References •
N. Henry, J-D. Fekete, M. Mcguffin. NodeTrix: Hybrid Representation for Analyzing Social Networks, Research Report 6183, INRIA, 2007. https://hal.inria.fr/inria-00144496
•
N. Henry and J-D. Fekete. MatLink: Enhanced Matrix Visualization for Analyzing Social Networks. In Processding of the eleventh IFIP TC13 International Conference on Human-Computer Interaction (Interact 2007), September 2007. Springer Verlag. 14 pages, to be published.
•
N. Henry and J-D. Fekete. MatrixExplorer: a Dual-Representation System to Explore Social Networks. IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2006), 12(5):677-684, September-October 2006.
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M. Ghoniem, J-D. Fekete and P. Castagliola. Readability of Graphs Using NodeLink and Matrix-Based Representations: Controlled Experiment and Statistical Analysis. Information Visualization Journal, 4(2):114–135, 2005.
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