1. A Discrepancy Detector James Abello, CCICADA-DIMACS FACULTY (www.mgvis.com) Student: Nishchal Devanur CS Dept Rutgers Goal To detect the most influential nodes in a variety of traffic scenarios using Discrepancy Theory.

2. Twitter Data: Nodes are twitter users. Edges exist between them if one user “retweeted” another user for the hashtag “#IranElection”

3. Manifest Data: Pattern Content Analysis

4. Manifest Data: Nodes represent days. Edges exist between days based on the shipment content similarity.

5. IPdataset: Nodes are Ip addresses and edges represent communication between the Ip addresses.

6. References 1.Graph View, James Abello, F. van Ham and N. Krishnan - "Ask Graph View", IEEE Transactions on Visualization and Computer Graphics, Vol 12. No 5, 2006 2.Gephi – http://gephi.orghttp://gephi.org 3.J. Abello, T. Eliassi-Rad, N. Devanur, “Detecting Novel Discrepancies in Communication Networks”, 10 th IEEE International Conference on Data Mining, ICDM10, Dec 2010, Australia