The US Army Research Office, DARPA, and a gift from Northrop Grumman Aerospace Systems. Her research is currently supported by the National Science Foundation, Recipient of the NSF CAREER award (2015) and Army Research Office Young InvestigatorĪward (2013). Research has won 4 publication awards Best Research Paper at SIAM SDM 2015, Best PaperĪt ADC 2014, Best Paper at PAKDD 2010, and Best Knowledge Discovery Paper at ECML/PKDD 2009. Problems arising in graph mining, pattern discovery, social and information networks,Īnd especially anomaly mining outlier, fraud, and event detection. Wide range of data mining and machine learning topics with a focus on algorithmic Labs and Microsoft Research at Redmond during summers. from the Computer Science DepartmentĪt Carnegie Mellon University in 2012. Leman Akoglu is an Assistant Professor in the Department of Computer Science at 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. We will then give brief examples of recent research using the techniques to model, understand and predict normal behavior. the "who-reviews-what" graphs of Amazon and Yelp.įor each of these techniques we will give an explanation of the algorithms and the intuition behind them. "who-calls-whom" networks, and attributed graphs, e.g. 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. Christos Faloutsos (Carnegie Mellon University, USA)Ībstract: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.Leman Akoglu (Stony Brook University, USA).Alex Beutel (Carnegie Mellon University, USA).Tutorial Abstracts and Lecturer Resumes Tutorial 1: Fraud Detection through Graph-Based User Behavior Modeling Lecturers:
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