Fraud Detection through Graph-Based User Behavior Modeling


How do anomalies, fraud, and spam effect our models of normal user behavior? How can we modify our models to catch fraudsters? In this tutorial we will answer these questions - connecting graph analysis tools for user behavior modeling to anomaly and fraud detection. In particular, we will focus on three data mining techniques: subgraph analysis, label propagation and latent factor models; and their application to static graphs, e.g. social networks, evolving graphs, e.g. "who-calls-whom" networks, and attributed graphs, e.g. the "who-reviews-what" graphs of Amazon and Yelp. For each of these techniques we will give an explanation of the algorithms and the intuition behind them. We will then give brief examples of recent research using the techniques to model, understand and predict normal behavior. With this intuition for how these methods are applied to graphs and user behavior, we will focus on state-of-the-art research showing how the outcomes of these methods are effected by fraud, and how they have been used to catch fraudsters.

DOI: 10.1145/2810103.2812702

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@inproceedings{Beutel2015FraudDT, title={Fraud Detection through Graph-Based User Behavior Modeling}, author={Alex Beutel and Leman Akoglu and Christos Faloutsos}, booktitle={ACM Conference on Computer and Communications Security}, year={2015} }