Research Papers and Links
Papers
On visualizing relational data as maps
- E. Gansner, Y. Hu, S. G. Kobourov, and C. Volinsky, "Putting Recommendations on the Map - Visualizing Clusters and Relations," 3rd ACM Conference on Recommendation Systems, p. 345-354, 2009.
- E. Gansner, Y. Hu, and S. G. Kobourov, "GMap: Visualizing Graphs and Clusters as Maps", 3rd IEEE Pacific Visualzation Symposium, p. 201-208, 2010.
- Y. Hu, S. G. Kobourov, and D. Mashima, "Visualizing Dynamic Data with Maps," 4th IEEE Pacific Visualzation Symposium (PacificVis), p. 102-110, 2011.
- E. Gansner, Y. Hu, and S. G. Kobourov, "Viewing Abstract Data as Maps"
- Y. Hu, S. G. Kobourov, S. Veeramoni, "Embedding, Clustering and Coloring for Dynamic Maps," Journal of Graph Algorithms and Applications, vol. 18, no. 1, p. 77-109, 2014.
- J. Alam, M. Kaufmann, S. G. Kobourov, and T. Mchedlidze, "Fitting Planar Graphs on Planar Maps," 40th Conference on Current Trends in Theory and Practice of Computer Science (SofSem), p. 52-64, 2014.
- Bahardor Saket, Paolo Simonetto, and Stephen Kobourov "Group-Level Graph Visualization Taxonomy," arXiv
- Bahardor Saket, Paolo Simonetto, Stephen Kobourov, and Kay Borner "Node, Node-Link, and Node-Link-Group Diagrams: An Evaluation," arXiv
- Monowar H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita "Network Anomaly Detection: Methods, Systems and Tools" IEEE Communications Surveys & Tutorials, vol. 16, no. 1, first quarter 2014
- Varun Chandola, Arindam Banerjee, AND Vipin Kumar "Anomaly Detection: A Survey." ACM Comput. Surv. 41, 3, Article 15 (July 2009), 58 pages. DOI=10.1145/1541880.1541882
- Gautam Thatte, Member, IEEE, Urbashi Mitra, Fellow, IEEE, and John Heidemann, Senior Member, IEEE, ACM "Parametric methods for anomoly detection in aggregate traffic," IEEE/ACM Transactions on Networking, vol. 19, no. 2, april 2011
- Jing Wang, Daniel Rossel, Christos G. Cassandras, Ioannis Ch. Paschalidis "Network anomaly detection: A Survey and Comparative Analysis of Stochastic and Deterministic Methods,"
Example Maps
Maps modeling similarities for different media
- GMAP of Arizona The GMAP tool for visualizing graphs as maps.
- TVLand - common viewership patterns of the 1000 most watched TV shows using data from over a million digital TV set top boxes
- MovieLand (Large Image) - related predictions of movie preferences for Netflix , using sample data provided as part of the Netflix Prize
- MusicLand - correlations of recommendations of the 2588 most listened to musicians on last.fm
- DynamicMusicLand Live example of music land map. last.fm
- Books related to 'Harry Potter and the Sorcerer's Stone' - associated books from web crawl of "Customers who bought this item also bought" links on Amazon
Maps modeling real-world data
- World Wide Web - based on xmarks.com data
Related Websites
AT&T Research