# Anomaly Detection in Graph Structured Data: A Survey

arXiv: https://arxiv.org/abs/2405.06172 · HTML: https://mlsanity.com/paper/2405.06172/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 120 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 30 of 33)

- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [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)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [A Semi-supervised Graph Attentive Network for Financial Fraud Detection](https://mlsanity.com/paper/2003.01171/) (2020)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [Graph Embedded Pose Clustering for Anomaly Detection](https://mlsanity.com/paper/1912.11850/) (2019)
- [Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection](https://mlsanity.com/paper/2005.00625/) (2020)
- [Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation](https://mlsanity.com/paper/2112.10063/) (2021)
- [Anomaly Detection in Dynamic Graphs via Transformer](https://mlsanity.com/paper/2106.09876/) (2021)
- [Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs](https://mlsanity.com/paper/2005.07427/) (2020)
- [GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection](https://mlsanity.com/paper/2005.10150/) (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)
- [Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs](https://mlsanity.com/paper/1602.04844/) (2016)
- [SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks](https://mlsanity.com/paper/1908.03849/) (2019)
- [Scalable Anomaly Ranking of Attributed Neighborhoods](https://mlsanity.com/paper/1601.06711/) (2016)
- [One-Class Adversarial Nets for Fraud Detection](https://mlsanity.com/paper/1803.01798/) (2018)
- [MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams](https://mlsanity.com/paper/1911.04464/) (2019)
- [F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams](https://mlsanity.com/paper/2011.04723/) (2020)
- [Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach](https://mlsanity.com/paper/2011.13085/) (2020)
- [A Survey on Social Media Anomaly Detection](https://mlsanity.com/paper/1601.01102/) (2016)
- [MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams](https://mlsanity.com/paper/2009.08451/) (2020)
- [Anomaly detection in online social networks](https://mlsanity.com/paper/1608.00301/) (2016)
- [Detecting change points in the large-scale structure of evolving networks](https://mlsanity.com/paper/1403.0989/) (2014)
- [Mining Persistent Activity in Continually Evolving Networks](https://mlsanity.com/paper/2006.15410/) (2020)
- [Improving Cyberbully Detection with User Interaction](https://mlsanity.com/paper/2011.00449/) (2020)
- [Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection](https://mlsanity.com/paper/2005.07886/) (2020)
- [Real-Time Anomaly Detection in Edge Streams](https://mlsanity.com/paper/2009.08452/) (2020)

## Built on by (most cited first, 3 of 3)

- [Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication](https://mlsanity.com/paper/2501.17329/) (2025)
- [A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection](https://mlsanity.com/paper/2510.26307/) (2025)
- [Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention](https://mlsanity.com/paper/2603.10676/) (2026)

## Abstract (arXiv)

Real-world graphs are complex to process for performing effective analysis, such as anomaly detection. However, recently, there have been several research efforts addressing the issues surrounding graph-based anomaly detection. In this paper, we discuss a comprehensive overview of anomaly detection techniques on graph data. We also discuss the various application domains which use those anomaly detection techniques. We present a new taxonomy that categorizes the different state-of-the-art anomaly detection methods based on assumptions and techniques. Within each category, we discuss the fundamental research ideas that have been done to improve anomaly detection. We further discuss the advantages and disadvantages of current anomaly detection techniques. Finally, we present potential future research directions in anomaly detection on graph-structured data.