Categorical relationships in history of infovis publications
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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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  • 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


  • 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,

Previous work
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

Previous work1
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


Want to relate publication network data with corresponding category information

Proposed visualization
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

Graphing publication networks
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

Visualizing categories
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


  • 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++)

Current progress
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