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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goals
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
dataset
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,
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
criticism
Criticism

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
implementation
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++)
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
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