NDLI: Anomaly, event, and fraud detection in large network datasets

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Anomaly, event, and fraud detection in large network datasets

Content Provider	ACM Digital Library
Author	Faloutsos, Christos Akoglu, Leman
Abstract	Detecting anomalies and events in data is a vital task, with numerous applications in security, finance, health care, law enforcement, and many others. While many techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, novel technology has been developed for abnormality detection in graph data. The goal of this tutorial is to provide a general, comprehensive overview of the state-of-the-art methods for anomaly, event, and fraud detection in data represented as graphs. As a key contribution, we provide a thorough exploration of both data mining and machine learning algorithms for these detection tasks. We give a general framework for the algorithms, categorized under various settings: unsupervised vs.(semi-)supervised, for static vs. dynamic data. We focus on the scalability and effectiveness aspects of the methods, and highlight results on crucial real-world applications, including accounting fraud and opinion spam detection.
Starting Page	773
Ending Page	774
Page Count	2
File Format	PDF
ISBN	9781450318693
DOI	10.1145/2433396.2433496
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-02-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Event detection Fraud Anomaly detection Graph mining
Content Type	Text
Resource Type	Article

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