The early bird gets the buzz: detecting anomalies and emerging trends in information networks

Authors:
Brian Thompson
Affiliations:
Rutgers University, Piscataway, NJ, USA
Venue:
Proceedings of the fifth ACM international conference on Web search and data mining
Year:
2012

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Abstract

In this work we propose a novel approach to anomaly detection in streaming communication data. We first build a stochastic model for the system based on temporal communication patterns across each edge, which we call the REWARDS (REneWal theory Approach for Real-time Data Streams) model. We then define a measure of anomaly for an arbitrary subgraph based on the likelihood of its recent activity given past behavior. Finally, we develop an algorithm to efficiently identify subgraphs with the most anomalous activity. Although our work has until now focused on the cybersecurity domain, the model we present is more broadly applicable to information retrieval in data streams and information networks.