Catherine Plaisant

Computer Vision and Pattern Recognition · 2002–2019

A researcher in the Science corpus: 14 papers; trusts 15 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

Whom Catherine Plaisant trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. B. Bederson
  2. Ben Shneiderman
  3. Lucy Suchman
  4. Jesse Grosjean
  5. Martin Wattenberg
  6. Neil Burgess
  7. Mohammed J. Zaki
  8. Jon Meyer
  9. Stuart Card
  10. Tamara Munzner

Who trusts Catherine Plaisant overall

  1. Tamara Munzner
  2. Ben Shneiderman
  3. Jean-Daniel Fekete
  4. Ryen W. White
  5. Shixia Liu
  6. Matthew Brehmer
  7. Heimo Müller
  8. Michael Sedlmair
  9. Sheelagh Carpendale
  10. Resa A. Roth

Papers from Catherine Plaisant's perspective

Ordered by how strongly the researchers Catherine Plaisant trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
  2. Toolkit design for interactive structured graphics (IEEE Transactions on Software Engineering)
  3. Readings in information visualization - using vision to think
  4. SpaceTree: supporting exploration in large node link tree, design evolution and empirical evaluation (IEEE Symposium on Information Visualization, 2002. INFOVIS 2002.)
  5. Jazz: an extensible zoomable user interface graphics toolkit in Java (ACM Symposium on User Interface Software and Technology)
  6. The InfoVis Toolkit (IEEE Information Visualization Conference)
  7. ThemeRiver: Visualizing Thematic Changes in Large Document Collections (IEEE Transactions on Visualization and Computer Graphics)
  8. Focus+context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations (IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings)
  9. An open architecture for collaborative filtering of netnews
  10. Aligning temporal data by sentinel events: discovering patterns in electronic health records (International Conference on Human Factors in Computing Systems)

Their papers