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Graph-Based User Behavior Modeling: From Prediction to Fraud Detection

Published: 10 August 2015 Publication History

Abstract

How can we model users' preferences? How do anomalies, fraud, and spam effect our models of normal users? 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 the application of subgraph analysis, label propagation, and latent factor models to static, evolving, and attributed graphs. For each of these techniques we will give a brief explanation of the algorithms and the intuition behind them. We will then give 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.

Supplementary Material

Part 1 of 2 (p23091.m4v)
Part 2 of 2 (p23092.m4v)

Cited By

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  • (2023)Nationwide Deployment and Operation of a Virtual Arrival Detection System in the WildIEEE/ACM Transactions on Networking10.1109/TNET.2022.319680631:2(574-589)Online publication date: Apr-2023
  • (2023)Enabling Fraud Prediction on Preliminary Data Through Information Density BoosterIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.330052318(5706-5720)Online publication date: 2023
  • (2023)Detecting Medical Insurance Fraud Using a Heterogeneous Information Network with a Multi-behavior PatternComputer Science and Education10.1007/978-981-99-2443-1_60(704-720)Online publication date: 14-May-2023
  • Show More Cited By

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Published In

cover image ACM Conferences
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2015
2378 pages
ISBN:9781450336642
DOI:10.1145/2783258
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2015

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Author Tags

  1. anomalous behavior
  2. fraud detection
  3. outlier detection
  4. recommendation systems
  5. user behavior modeling

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  • Tutorial

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KDD '15
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Acceptance Rates

KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

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Cited By

View all
  • (2023)Nationwide Deployment and Operation of a Virtual Arrival Detection System in the WildIEEE/ACM Transactions on Networking10.1109/TNET.2022.319680631:2(574-589)Online publication date: Apr-2023
  • (2023)Enabling Fraud Prediction on Preliminary Data Through Information Density BoosterIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.330052318(5706-5720)Online publication date: 2023
  • (2023)Detecting Medical Insurance Fraud Using a Heterogeneous Information Network with a Multi-behavior PatternComputer Science and Education10.1007/978-981-99-2443-1_60(704-720)Online publication date: 14-May-2023
  • (2022)Discover the Hidden Attack Path in Multiple Domain Cyberspace Based on Reinforcement LearningScientific Programming10.1155/2022/60084472022Online publication date: 1-Jan-2022
  • (2022)CAeSaR: An Online Payment Anti-Fraud Integration System With Decision ExplainabilityIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.3186733(1-14)Online publication date: 2022
  • (2022)Neurally-Guided Semantic Navigation in Knowledge GraphIEEE Transactions on Big Data10.1109/TBDATA.2018.28053638:3(607-615)Online publication date: 1-Jun-2022
  • (2022)Invertible Neural Networks for Graph PredictionIEEE Journal on Selected Areas in Information Theory10.1109/JSAIT.2022.32218643:3(454-467)Online publication date: Sep-2022
  • (2022)Artificial Intelligence and Fraud DetectionInnovative Technology at the Interface of Finance and Operations10.1007/978-3-030-75729-8_8(223-247)Online publication date: 1-Jan-2022
  • (2021)Nationwide deployment and operation of a virtual arrival detection system in the wildProceedings of the 2021 ACM SIGCOMM 2021 Conference10.1145/3452296.3472911(705-717)Online publication date: 9-Aug-2021
  • (2020)A Graph-Based Method for Health Care Joint Fraud DetectionProceedings of the 2020 9th International Conference on Computing and Pattern Recognition10.1145/3436369.3437443(122-129)Online publication date: 30-Oct-2020
  • Show More Cited By

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