event, and fraud detection in large network datasets

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 anoma-lies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for struc-tured 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, compre-hensive 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 algo-rithms, 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