Anomaly Detection using Proximity Graph and PageRank Algorithm

TitleAnomaly Detection using Proximity Graph and PageRank Algorithm
Publication TypeJournal Article
Year of Publication2012
AuthorsYao, Z., P. Mark, and M. G. Rabbat
JournalIEEE Transactions on Information Forensics and Security
Volume7
Issue4
Start Page1288
Date Published08/2012
KeywordsAnomaly Detection, Personalized PageRank, Proximity Graph
Abstract

Anomaly detection techniques are widely used in a variety of applications, e.g., computer networks, security systems, etc. This paper describes and analyzes an approach to anomaly detection using proximity graphs and the PageRank algorithm. We run a variant of the PageRank algorithm on top of a proximity graph comprised of data points as vertices, which produces a score quantifying the extent to which each data point is anomalous. Previous work in this direction requires first forming a density estimate using the training data, e.g., using kernel methods, and this step is very computationally intensive for high-dimensional data sets. Under mild assumptions and appropriately chosen parameters, we show that PageRank produces point-wise consistent probability density estimates for the data points in an asymptotic sense, and with much less computational effort. As a result, big improvements in terms of running time are witnessed while maintaining similar detection performance. Experiments with synthetic and real-world data sets illustrate that the proposed approach is computationally tractable and scales well to large high-dimensional data sets.

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