# Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges

arXiv: https://arxiv.org/abs/2209.14930 · HTML: https://mlsanity.com/paper/2209.14930/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 66 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 30 of 34)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Hyperbolic Graph Convolutional Neural Networks](https://mlsanity.com/paper/1910.12933/) (2019)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [GRAND: Graph Neural Diffusion](https://mlsanity.com/paper/2106.10934/) (2021)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Data Augmentation for Graph Neural Networks](https://mlsanity.com/paper/2006.06830/) (2020)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2103.00113/) (2021)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Self-supervised Learning on Graphs: Deep Insights and New Direction](https://mlsanity.com/paper/2006.10141/) (2020)
- [Neural Transformation Learning for Deep Anomaly Detection Beyond Images](https://mlsanity.com/paper/2103.16440/) (2021)
- [AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks](https://mlsanity.com/paper/2002.03665/) (2020)
- [Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection](https://mlsanity.com/paper/2108.09896/) (2021)
- [Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation](https://mlsanity.com/paper/2112.10063/) (2021)
- [Few-shot Network Anomaly Detection via Cross-network Meta-learning](https://mlsanity.com/paper/2102.11165/) (2021)
- [Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs](https://mlsanity.com/paper/2005.07427/) (2020)
- [Graph Geometry Interaction Learning](https://mlsanity.com/paper/2010.12135/) (2020)
- [On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insights](https://mlsanity.com/paper/2012.12931/) (2020)
- [Raising the Bar in Graph-level Anomaly Detection](https://mlsanity.com/paper/2205.13845/) (2022)
- [SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks](https://mlsanity.com/paper/1908.03849/) (2019)
- [Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2104.07917/) (2021)
- [ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2009.14738/) (2020)

## Built on by (most cited first, 23 of 23)

- [Graph Anomaly Detection in Time Series: A Survey](https://mlsanity.com/paper/2302.00058/) (2023)
- [Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey](https://mlsanity.com/paper/2503.13195/) (2025)
- [Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection](https://mlsanity.com/paper/2403.12172/) (2024)
- [Graph Neural Networks based Log Anomaly Detection and Explanation](https://mlsanity.com/paper/2307.00527/) (2023)
- [Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2312.05526/) (2023)
- [Graph Neural Network for spatiotemporal data: methods and applications](https://mlsanity.com/paper/2306.00012/) (2023)
- [Towards Personalized Quantum Federated Learning for Anomaly Detection](https://mlsanity.com/paper/2511.07471/) (2025)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement](https://mlsanity.com/paper/2306.10857/) (2023)
- [Computationally-efficient Graph Modeling with Refined Graph Random Features](https://mlsanity.com/paper/2510.07716/) (2025)
- [Label-based Graph Augmentation with Metapath for Graph Anomaly Detection](https://mlsanity.com/paper/2308.10918/) (2023)
- [SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2401.01625/) (2024)
- [Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective](https://mlsanity.com/paper/2505.18002/) (2025)
- [A General ReLearner: Empowering Spatiotemporal Prediction by Re-learning Input-label Residual](https://mlsanity.com/paper/2602.02563/) (2026)
- [Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization](https://mlsanity.com/paper/2605.28428/) (2026)
- [Self-supervised Learning for Anomaly Detection in Computational Workflows](https://mlsanity.com/paper/2310.01247/) (2023)
- [Robust Subgraph Learning by Monitoring Early Training Representations](https://mlsanity.com/paper/2403.09901/) (2024)
- [Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2404.16366/) (2024)
- [A Methodological Report on Anomaly Detection on Dynamic Knowledge Graphs](https://mlsanity.com/paper/2408.06121/) (2024)
- [Spatially Regularized Graph Attention Autoencoder Framework for Detecting Rainfall Extremes](https://mlsanity.com/paper/2411.07753/) (2024)
- [Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning](https://mlsanity.com/paper/2503.12037/) (2025)
- [Mathematical Insights into Protein Architecture: Persistent Homology and Machine Learning Applied to the Flagellar Motor](https://mlsanity.com/paper/2504.16941/) (2025)
- [Robust Anomaly Detection with Graph Neural Networks using Controllability](https://mlsanity.com/paper/2507.13954/) (2025)

## Abstract (arXiv)

Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems. Graph anomalies are patterns in a graph that do not conform to normal patterns expected of the attributes and/or structures of the graph. In recent years, graph neural networks (GNNs) have been studied extensively and have successfully performed difficult machine learning tasks in node classification, link prediction, and graph classification thanks to the highly expressive capability via message passing in effectively learning graph representations. To solve the graph anomaly detection problem, GNN-based methods leverage information about the graph attributes (or features) and/or structures to learn to score anomalies appropriately. In this survey, we review the recent advances made in detecting graph anomalies using GNN models. Specifically, we summarize GNN-based methods according to the graph type (i.e., static and dynamic), the anomaly type (i.e., node, edge, subgraph, and whole graph), and the network architecture (e.g., graph autoencoder, graph convolutional network). To the best of our knowledge, this survey is the first comprehensive review of graph anomaly detection methods based on GNNs.