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Categorical Relationships in History of InfoVis Publications

Categorical Relationships in History of InfoVis Publications. CS533C Project Update by Alex Gukov. Goals. Provide visual overview of InfoVis publication history Author collaboration network Paper co-citation network Identify key influences Major research categories

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Categorical Relationships in History of InfoVis Publications

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  1. Categorical Relationships in History of InfoVis Publications CS533C Project Update by Alex Gukov

  2. Goals • Provide visual overview of InfoVis publication history • Author collaboration network • Paper co-citation network • Identify key influences • Major research categories • Influential authors and papers within a categories • Related categories

  3. Dataset • Filtered 2004 InfoVis Contest data • InfoVis publication history from 1995 to 2002 • Original data cleaned up by Indiana University contestants • Medium size network • 614 InfoVis articles with detailed metadata • 8502 references with limited metadata • 1036 authors • Paper metadata • Title, year, abstract, keywords,

  4. Previous Work • Indiana University contest entry • Node link diagram of highly cited papers, published authors • Clearly identifies important papers, authors through node size • Does not relate authors, papers to research categories

  5. Previous Work • IN-SPIRE by Pacific Northwest National Laboratory • Scatter plot of publications • Plot positioning based on themes extracted from metadata • Clearly identifies dominant themes • Does not make use of citation data

  6. Criticism Want to relate publication network data with corresponding category information

  7. Proposed Visualization • Reduce the data set by using highly cited papers, published authors • Visualize collaboration and co-citation networks with node-link graphs • Augment the plots with category information using background color

  8. Graphing publication networks • Node-link diagrams with papers, authors as graph nodes • Node size proportional to the number of received citations • Node color • Paper publication date • Number of papers written by an author • Force-directed layout using topology and category information as cues

  9. Visualizing categories • Identify a small number of categories from paper metadata • Process titles, abstracts, keywords • Reduce dimensionality, cluster • Partition space around the graph nodes after layout is complete • Color the background of each node with corresponding category ( use light colors ) • Author is assigned the mean category of his/her publications

  10. Mockup

  11. Implementation • Category identification • Use PCA to reduce noise • Use k-means on the resulting data • Graph visualization • Prefuse Visualization toolkit(Java) • Built-in force-directed layout engine • Background space partitioning • Partition using a Voronoi diagram • Use CGAL geometry toolkit (C++)

  12. Current Progress • Data graphing • Setup Prefuse and experimented with a sample social network graphing application • Category identification • Access database converted to xml • Preprocessing data for use in Matlab

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