TgrApp: Anomaly Detection and Visualization of Large-Scale Call Graphs

Mirela T. Cazzolato, Saranya Vijayakumar, Xinyi Zheng, Namyong Park, Meng-Chieh Lee, Duen Horng Chau, Pedro Fidalgo, Bruno Lages, Agma J. M. Traina, Christos Faloutsos

AAAI 2023 pp. 16410-16412

doi:10.1609/AAAI.V37I13.27062 /aaai/2023/cazzolato2023aaai-tgrapp/

Abstract

Given a million-scale dataset of who-calls-whom data containing imperfect labels, how can we detect existing and new fraud patterns? We propose TgrApp, which extracts carefully designed features and provides visualizations to assist analysts in spotting fraudsters and suspicious behavior. Our TgrApp method has the following properties: (a) Scalable, as it is linear on the input size; and (b) Effective, as it allows natural interaction with human analysts, and is applicable in both supervised and unsupervised settings.

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Cite

Text

Cazzolato et al. "TgrApp: Anomaly Detection and Visualization of Large-Scale Call Graphs." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.27062

Markdown

[Cazzolato et al. "TgrApp: Anomaly Detection and Visualization of Large-Scale Call Graphs." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/cazzolato2023aaai-tgrapp/) doi:10.1609/AAAI.V37I13.27062

BibTeX

@inproceedings{cazzolato2023aaai-tgrapp,
  title     = {{TgrApp: Anomaly Detection and Visualization of Large-Scale Call Graphs}},
  author    = {Cazzolato, Mirela T. and Vijayakumar, Saranya and Zheng, Xinyi and Park, Namyong and Lee, Meng-Chieh and Chau, Duen Horng and Fidalgo, Pedro and Lages, Bruno and Traina, Agma J. M. and Faloutsos, Christos},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16410-16412},
  doi       = {10.1609/AAAI.V37I13.27062},
  url       = {https://mlanthology.org/aaai/2023/cazzolato2023aaai-tgrapp/}
}