NDLI: Efficient anomaly detection in dynamic, attributed graphs: Emerging phenomena and big data

Content Provider	IEEE Xplore Digital Library
Author	Miller, B.A. Arcolano, N. Bliss, N.T.
Copyright Year	2013
Description	Author affiliation: Arizona State Univ., Tempe, AZ, USA (Bliss, N.T.) \|\| Lincoln Lab., Massachusetts Inst. of Technol., Lexington, MA, USA (Miller, B.A.; Arcolano, N.)
Abstract	When working with large-scale network data, the interconnected entities often have additional descriptive information. This additional metadata may provide insight that can be exploited for detection of anomalous events. In this paper, we use a generalized linear model for random attributed graphs to model connection probabilities using vertex metadata. For a class of such models, we show that an approximation to the exact model yields an exploitable structure in the edge probabilities, allowing for efficient scaling of a spectral framework for anomaly detection through analysis of graph residuals, and a fast and simple procedure for estimating the model parameters. In simulation, we demonstrate that taking into account both attributes and dynamics in this analysis has a much more significant impact on the detection of an emerging anomaly than accounting for either dynamics or attributes alone. We also present an analysis of a large, dynamic citation graph, demonstrating that taking additional document metadata into account emphasizes parts of the graph that would not be considered significant otherwise.
Sponsorship	IEEE Intelligent Transp. Syst. Soc.
Starting Page	179
Ending Page	184
File Size	461410
Page Count	6
File Format	PDF
ISBN	9781467362146
e-ISBN	9781467362139
DOI	10.1109/ISI.2013.6578815
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	generalized linear models Analytical models Image edge detection Computational modeling signal detection theory Vectors Data models network modularity Sparse matrices Approximation methods attributed graph modeling Subgraph detection
Content Type	Text
Resource Type	Article

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