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.

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

Manifest Data: Pattern Content Analysis

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

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

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.org3.J. Abello, T. Eliassi-Rad, N. Devanur, “Detecting

Novel Discrepancies in Communication Networks”, 10th IEEE International Conference on Data Mining, ICDM10, Dec 2010, Australia