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Exploring Small-World Networks and Node Attributes in Data Visualization

This document discusses the intricacies of small-world networks characterized by high clustering coefficients and short average path lengths, exemplified by classic studies like Milgram's 1967 research. It covers advanced graphical techniques like force-directed layouts and Targeted Projection Pursuit (TPP) for exploring high-dimensional data sets. The significance of node attributes, including retinal variables like color, size, and shape, is examined. The study also highlights tools like Gephi for interactive network exploration and emphasizes the importance of empirical validation in real-world applications.

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Exploring Small-World Networks and Node Attributes in Data Visualization

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  1. Node-Attribute Graph Layout for Small-World Networks Helen Gibson Joe Faith IV2011 - AGT Intelligent Modelling Lab

  2. Small-World Networks What are they? • Clustered with a high clustering coefficient • Smaller than average shortest path length Examples • Milgram (1967) • IMDB Layout • Force Directed • Packed together • Lose clusters • Users IV2011 - AGT Intelligent Modelling Lab

  3. Node-Attributes Information about the nodes • Retinal Variables • Colour • Size • Shape What about nodes having multiple classifications? Or lots of quantitative attributes? IV2011 - AGT Intelligent Modelling Lab

  4. Node Attributes Attributes (O) Nodes (X) IV2011 - AGT Intelligent Modelling Lab

  5. Dimension Reduction + TPP Targeted Projection Pursuit – interactive high-dimensional data exploration 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. IV2011 - AGT Intelligent Modelling Lab

  6. Example Application Gephi Randomly add and remove links • Force-directed • Yifan Hu Clustered Remove attributes Assign attributes gephi.org IV2011 - AGT Intelligent Modelling Lab

  7. Example Application Targeted Projection Pursuit • Attributes as dimensions • Number of attributes = • Number of dimensions Which attributes are significant in clustering? http://code.google.com/p/targeted-projection-pursuit/ IV2011 - AGT Intelligent Modelling Lab

  8. Example Application LinLog - Andreas Noack (2007) • Energy Models • Force Directed • Graph Clusterings http://code.google.com/p/linloglayout/ IV2011 - AGT Intelligent Modelling Lab

  9. Conclusions + Further Work • 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/ IV2011 - AGT Intelligent Modelling Lab

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