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Readability Metrics for Network Visualization
 

Readability Metrics for Network Visualization

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  • This image shows a subset of the ACL Anthology Network of academic papers that deal with dependency parsing. There are five coordinated views depicted here. The left side is your reference manager (JabRef, similar to Endnote, Mendeley, etc.) that gives a list of papers and their abstracts, BibTeX, doi/url/PDF links and the like and allows you to search, sort, group, and import papers. The middle window is the citation network visualization (SocialAction) which shows the node-link diagram for the citation network. Automatically generated clusters (Newman's fast heuristic) are shown with convex hulls and rankings by statistical measures like in-degree, betweenness centrality, and the like are shown in the left 1/3 and color coded in the visualization.The bottom-middle pane displays the text of incoming citations for the selected papers. It was automatically extracted from each paper that cites the selected paper. Selected papers are highlighted in yellow in the network visualization and shown with white backgrounds instead of gray in the reference manager. The lime colored selection of in-cite text is a citation we want to see the context of in the citing paper, which is shown in the top-right. The selection is highlighted in lime there as well, and other automatically extracted citations are shown in orange and purple and can be clicked on to select the target papers.The bottom-right pane shows an automatically generated summary of the in-cite text of the selected papers using a currently poor summarization algorithm.
  • This image shows a subset of the ACL Anthology Network of academic papers that deal with dependency parsing. There are five coordinated views depicted here. The left side is your reference manager (JabRef, similar to Endnote, Mendeley, etc.) that gives a list of papers and their abstracts, BibTeX, doi/url/PDF links and the like and allows you to search, sort, group, and import papers. The middle window is the citation network visualization (SocialAction) which shows the node-link diagram for the citation network. Automatically generated clusters (Newman's fast heuristic) are shown with convex hulls and rankings by statistical measures like in-degree, betweenness centrality, and the like are shown in the left 1/3 and color coded in the visualization.The bottom-middle pane displays the text of incoming citations for the selected papers. It was automatically extracted from each paper that cites the selected paper. Selected papers are highlighted in yellow in the network visualization and shown with white backgrounds instead of gray in the reference manager. The lime colored selection of in-cite text is a citation we want to see the context of in the citing paper, which is shown in the top-right. The selection is highlighted in lime there as well, and other automatically extracted citations are shown in orange and purple and can be clicked on to select the target papers.The bottom-right pane shows an automatically generated summary of the in-cite text of the selected papers using a currently poor summarization algorithm.
  • This image shows a subset of the ACL Anthology Network of academic papers that deal with dependency parsing. There are five coordinated views depicted here. The left side is your reference manager (JabRef, similar to Endnote, Mendeley, etc.) that gives a list of papers and their abstracts, BibTeX, doi/url/PDF links and the like and allows you to search, sort, group, and import papers. The middle window is the citation network visualization (SocialAction) which shows the node-link diagram for the citation network. Automatically generated clusters (Newman's fast heuristic) are shown with convex hulls and rankings by statistical measures like in-degree, betweenness centrality, and the like are shown in the left 1/3 and color coded in the visualization.The bottom-middle pane displays the text of incoming citations for the selected papers. It was automatically extracted from each paper that cites the selected paper. Selected papers are highlighted in yellow in the network visualization and shown with white backgrounds instead of gray in the reference manager. The lime colored selection of in-cite text is a citation we want to see the context of in the citing paper, which is shown in the top-right. The selection is highlighted in lime there as well, and other automatically extracted citations are shown in orange and purple and can be clicked on to select the target papers.The bottom-right pane shows an automatically generated summary of the in-cite text of the selected papers using a currently poor summarization algorithm.

Readability Metrics for Network Visualization Readability Metrics for Network Visualization Presentation Transcript

  • iOpener Workbench: Tools for Rapid Understanding of Scientific Literature
    Cody Dunne, Ben Shneiderman, Bonnie Dorr & Judith Klavans
    {cdunne, ben, bonnie}@cs.umd.edu, jklavans@umd.edu
    27th Annual Human-Computer Interaction Lab Symposium
    May 27-28, 2010College Park, MD
  • iOpener Workbench
  • Contribution
    Infrastructure for rapidly summarizing scientific endeavor
    Integrate statistics, visualization, reference management, and automatic summarization
    Multiple coordinated views
  • Use Cases
    Learn about new fields
    Understand how communities form
    Analyze citation patterns within communities
    Easily explore & export all papers in a community
  • What we integrate
    Potent network analysis tool – SocialAction
    Citation network statistics & visualization
    Automatic community detection & visualization
    Reference & document management – JabRef
    Powerful reference manager with extensive features for search, grouping, review, annotation, and export
    Document view with citation linking & highlight
    Automatically generated summaries
    Citationtext, keywords, abstracts
  • What can you do with a graph?
    Statistics, lists, and text is helpful, but
    Visualizations show unexpected trends, clusters, gaps, outliers
    Data cleaning & verification
    “Information visualization answers questions you didn't know you had” – Ben S.
  • Importance of Survey Articles
    Rapidly expanding disciplines
    Large volume of scientific publications
    Increasing cross-disciplinary research
    Need for accurate surveys of previous work
    Short summaries
    In-depth historical notes
    Multiple users
    Scientists
    Students & Educators
    Government decision makers
  • iOPENER
    NSF Info Integration & Informatics program
    Information Organization for PENningExpositions on Research
  • Components
    Bibliometriclexical link mining
    Automatic summarization techniques
    Visualization tools for structure and content
  • Ongoing Work
    Increase preprocessing of citation texts to vastly improve trimmer summary comprehension
    Preliminary case studies with UMD student domain experts
    Dependency parsing subset of the ACL Anthology Network (AAN)
  • Coming Soon
    Multi-dimensional in-depth long-term case studies
    longitudinal case studies with domain experts using their data
    close participant observation
    Software & generated surveys publicly available and presented to academia and wider audiences
  • iOpener Workbench
    Infrastructure to aid rapid summarization of scientific literature
    Integrates
    Statistics
    Visualization
    Reference management
    Automatic summarization
  • iOpener Workbench: Tools for Rapid Understanding of Scientific Literature
    Cody Dunne, Ben Shneiderman, Bonnie Dorr & Judith Klavans
    {cdunne, ben, bonnie}@cs.umd.edu, jklavans@umd.edu
    tangra.si.umich.edu/clair/iopener
    This work has been partially supported by NSF grant "iOPENER: A Flexible Framework to Support Rapid Learning in Unfamiliar Research Domains", jointly awarded to UMD and UMich as IIS 0705832.
  • Network Analysis
  • Reference Manager
  • Document & Citation View
  • Summarization
  • Features – Network analysis
    SocialAction (Perer, Shneiderman)
    Citation networkvisualization
    Force-directed placement (by linkages)
    Scatterplots of paper attributes & statistics
    Statistics ranktables
    Categorial and numerical range coloring
    Automatic community detection
    Newman '04 fast heuristic
  • Features – Reference Manager
    Search by field with simple regex
    abstract|keywords=nonprojective and year = 2008
    Grouping-- automatic, search results, manual
    DOI/URL, fulltext (annotated PDF, plain text)
    Metadata, abstracts
    User generated reviews
    BibTeX, Word, OpenOfficeintegration
    HTML, EndNote export
  • Document view - features
    Citation links
    Highlighting
  • Summarization - Features
    Automatically generated summariesCitationtext, keywords, abstracts
    Working to substantially improve coherence & relevance