Anomaly Detection using Graph Neural Networks
Abstract
Conventional methods for anomaly detection include techniques based on clustering, proximity or classification. With the rapidly growing social networks, outliers or anomalies find ingenious ways to obscure themselves in the network and making the conventional techniques inefficient. In this paper, we utilize the ability of Deep Learning over topological characteristics of a social network to detect anomalies in email network and twitter network. We present a model, Graph Neural Network, which is applied on social connection graphs to detect anomalies. The combinations of various social network statistical measures are taken into account to study the graph structure and functioning of the anomalous nodes by employing deep neural networks on it. The hidden layer of the neural network plays an important role in finding the impact of statistical measure combination in anomaly detection.
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Other deep neural networks such as Graph Neural Networks and Transformers have become prevalent in anomaly detection.
“In addition to the above-mentioned DNNs, namely, AEs, RNNs, CNNs, GANs, Deep OCSVM, and DevNet, other DNNs such as Graph Neural Networks [39] and Transformers [119] have become prevalent in anomaly detection.”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [39]
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Reconstructed Graph Neural Network With Knowledge Distillation for Lightweight Anomaly Detection
Zhou, X., Wu, J., Liang, W., Wang, K. I.-K., Yan, Z., Yang, L. T., & Jin, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3389714 · Cited by 145 · see citing works
A Survey on Explainable Anomaly Detection
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“Discussion: In addition to the above mentioned DNNs, namely AEs, RNNs, CNNs, GANs, Deep OCSVM and DevNet, other DNNs such as Graph Neural Networks [39] and Transformers [119] have become prevalent in anomaly detection.”
Background
- Authors
- Chaudhary, A., Mittal, H., & Arora, A.
- Affiliations
- Jaypee Institute of Information Technology (IN)
- ORCID
- Arora, Anuja
- Venue
- 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon)
- Publisher
- IEEE
- Published
- 2019-02
- Pages
- 346-350
- OpenAlex
- W2980013009
- Cited by
- 106
- References
- 24
- Citations by year
- 2026: 9 · 2025: 9 · 2024: 17 · 2023: 18 · 2022: 14 · 2021: 35 · 2020: 4
- FWCI
- 5.07
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- anomaly detection, graph neural networks, social networks, email networks, Twitter, graph structure, hidden layers, outlier detection
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