A path algorithm for localizing anomalous activity in graphs

• Published in: 2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP) pp. 341 - 344
• Main Author: Sharpnack, James
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2013
• Subjects: Capacitance; Computational modeling; Computer vision; Kernel; Markov processes; Social network services; Surveillance
• DOI: 10.1109/GlobalSIP.2013.6736885

The localization of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity monitoring in social networks. We will address the localization of a cluster of activity in Gaussian noise in directed, weighted graphs. We develop a penalized likelihood estimator (we call the relaxed graph scan) as a relaxation of the NP-hard graph scan statistic. We review how the relaxed graph scan (RGS) can be solved using graph cuts, and outline the max-flow min-cut duality. We use this combinatorial duality to derive a path algorithm for the RGS by solving successive max flows. We demonstrate the effectiveness of the RGS on two simulations, over an undirected and directed graph.