node-attribute graph layout for small-world networks
DESCRIPTION
Node-Attribute Graph Layout for Small-World Networks. Helen Gibson Joe Faith. IV2011 - AGT. Intelligent Modelling Lab. Small-World Networks. What are they?. Clustered with a high clustering coefficient Smaller than average shortest path length. Examples. Milgram (1967) IMDB. Layout. - PowerPoint PPT PresentationTRANSCRIPT
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Helen GibsonJoe Faith
Intelligent Modelling LabIV2011 - AGT
Node-Attribute Graph Layout for Small-World Networks
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Small-World Networks
Intelligent Modelling LabIV2011 - AGT
• Clustered with a high clustering coefficient
• Smaller than average shortest path length
What are they?
Layout
• Force Directed
• Packed together
• Lose clusters
• Users
Examples
• Milgram (1967)
• IMDB
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Node-Attributes
Intelligent Modelling LabIV2011 - AGT
Information about the nodes
• Retinal Variables
• Colour
• Size
• Shape
What about nodes having multiple classifications?
Or lots of quantitative attributes?
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Node Attributes
Intelligent Modelling LabIV2011 - AGT
Nodes (X)Attributes (O)
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Dimension Reduction + TPP
Intelligent Modelling LabIV2011 - AGT
J. Faith, “Targeted Projection Pursuit for Interactive Exploration of High- Dimensional Data Sets,” 11th International Conference Information Visualization (IV ’07), Jul. 2007, pp. 286-292.
Targeted Projection Pursuit – interactive high-dimensional data exploration
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Example Application
Intelligent Modelling LabIV2011 - AGT
gephi.org
Gephi
• Force-directed
• Yifan Hu
Clustered
Randomly add and remove links
Assign attributesRemove attributes
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Example Application
Intelligent Modelling LabIV2011 - AGT
Targeted Projection Pursuit
http://code.google.com/p/targeted-projection-pursuit/
• Attributes as dimensions
• Number of attributes =
Number of dimensions
Which attributes are significant in clustering?
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Example Application
Intelligent Modelling LabIV2011 - AGT
LinLog - Andreas Noack (2007)
http://code.google.com/p/linloglayout/
• Energy Models
• Force Directed
• Graph Clusterings
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Conclusions + Further Work
Intelligent Modelling LabIV2011 - AGT
• TPP - greater visual separation than force-directed layout
• TPP – doesn’t lose the context that LinLog does
But…
• Further empirical validation needed!
• Metrics
• Vary parameters
• Insights gained
• Further use of attributes
Most importantly…
• Real world applications
http://code.google.com/p/targeted-projection-pursuit/