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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 6: AAAI-22 Technical Tracks 6

LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks

February 1, 2023

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Authors

Adam Goodge

School of Computing, National University of Singapore Institute for Infocomm Research, A*STAR, Singapore


Bryan Hooi

School of Computing, National University of Singapore Institute of Data Science, National University of Singapore


See-Kiong Ng

School of Computing, National University of Singapore Institute of Data Science, National University of Singapore


Wee Siong Ng

Institute for Infocomm Research, A*STAR, Singapore


DOI:

10.1609/aaai.v36i6.20629


Abstract:

Many well-established anomaly detection methods use the distance of a sample to those in its local neighbourhood: so-called `local outlier methods', such as LOF and DBSCAN. They are popular for their simple principles and strong performance on unstructured, feature-based data that is commonplace in many practical applications. However, they cannot learn to adapt for a particular set of data due to their lack of trainable parameters. In this paper, we begin by unifying local outlier methods by showing that they are particular cases of the more general message passing framework used in graph neural networks. This allows us to introduce learnability into local outlier methods, in the form of a neural network, for greater flexibility and expressivity: specifically, we propose LUNAR, a novel, graph neural network-based anomaly detection method. LUNAR learns to use information from the nearest neighbours of each node in a trainable way to find anomalies. We show that our method performs significantly better than existing local outlier methods, as well as state-of-the-art deep baselines. We also show that the performance of our method is much more robust to different settings of the local neighbourhood size.

Topics: AAAI

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HOW TO CITE:

Adam Goodge||Bryan Hooi||See-Kiong Ng||Wee Siong Ng LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks Proceedings of the AAAI Conference on Artificial Intelligence (2022) 6737-6745.

Adam Goodge||Bryan Hooi||See-Kiong Ng||Wee Siong Ng LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks AAAI 2022, 6737-6745.

Adam Goodge||Bryan Hooi||See-Kiong Ng||Wee Siong Ng (2022). LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 6737-6745.

Adam Goodge||Bryan Hooi||See-Kiong Ng||Wee Siong Ng. LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.6737-6745.

Adam Goodge||Bryan Hooi||See-Kiong Ng||Wee Siong Ng. 2022. LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks. "Proceedings of the AAAI Conference on Artificial Intelligence". 6737-6745.

Adam Goodge||Bryan Hooi||See-Kiong Ng||Wee Siong Ng. (2022) "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks", Proceedings of the AAAI Conference on Artificial Intelligence, p.6737-6745

Adam Goodge||Bryan Hooi||See-Kiong Ng||Wee Siong Ng, "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks", AAAI, p.6737-6745, 2022.

Adam Goodge||Bryan Hooi||See-Kiong Ng||Wee Siong Ng. "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.6737-6745.

Adam Goodge||Bryan Hooi||See-Kiong Ng||Wee Siong Ng. "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 6737-6745.

Adam Goodge||Bryan Hooi||See-Kiong Ng||Wee Siong Ng. LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks. AAAI[Internet]. 2022[cited 2023]; 6737-6745.


ISSN: 2374-3468


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Copyright 2022, Association for the Advancement of
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