# Generative Semi-supervised Graph Anomaly Detection

arXiv: https://arxiv.org/abs/2402.11887 · HTML: https://mlsanity.com/paper/2402.11887/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 72 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 30 of 40)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Deep Graph Contrastive Representation Learning](https://mlsanity.com/paper/2006.04131/) (2020)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Rethinking Graph Neural Networks for Anomaly Detection](https://mlsanity.com/paper/2205.15508/) (2022)
- [Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2103.00113/) (2021)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection](https://mlsanity.com/paper/2306.10347/) (2023)
- [Generative Cooperative Learning for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2203.03962/) (2022)
- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks](https://mlsanity.com/paper/2002.03665/) (2020)
- [Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection](https://mlsanity.com/paper/2005.00625/) (2020)
- [BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs](https://mlsanity.com/paper/2206.10071/) (2022)
- [A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect](https://mlsanity.com/paper/2401.16402/) (2024)
- [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)
- [DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection](https://mlsanity.com/paper/2207.03579/) (2022)
- [GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection](https://mlsanity.com/paper/2306.12251/) (2023)
- [TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis](https://mlsanity.com/paper/2210.09693/) (2022)
- [GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction](https://mlsanity.com/paper/2306.01951/) (2023)
- [Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection](https://mlsanity.com/paper/2306.00006/) (2023)
- [Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data](https://mlsanity.com/paper/2009.06847/) (2020)
- [Towards Self-Interpretable Graph-Level Anomaly Detection](https://mlsanity.com/paper/2310.16520/) (2023)
- [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)
- [Fence GAN: Towards Better Anomaly Detection](https://mlsanity.com/paper/1904.01209/) (2019)
- [PyGOD: A Python Library for Graph Outlier Detection](https://mlsanity.com/paper/2204.12095/) (2022)

## Built on by (most cited first, 22 of 22)

- [Deep Graph Anomaly Detection: A Survey and New Perspectives](https://mlsanity.com/paper/2409.09957/) (2024)
- [Semi-supervised Graph Anomaly Detection via Robust Homophily Learning](https://mlsanity.com/paper/2506.15448/) (2025)
- [Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection](https://mlsanity.com/paper/2512.18133/) (2025)
- [GCTAM: Global and Contextual Truncated Affinity Combined Maximization Model For Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2603.01806/) (2026)
- [Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection](https://mlsanity.com/paper/2606.28134/) (2026)
- [PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities](https://mlsanity.com/paper/2409.11618/) (2024)
- [GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers](https://mlsanity.com/paper/2411.17296/) (2024)
- [MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection](https://mlsanity.com/paper/2504.12970/) (2025)
- [Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories](https://mlsanity.com/paper/2510.12686/) (2025)
- [AP-OOD: Attention Pooling for Out-of-Distribution Detection](https://mlsanity.com/paper/2602.06031/) (2026)
- [Position: Untrained Machine Learning for Anomaly Detection by using 3D Point Cloud Data](https://mlsanity.com/paper/2502.03876/) (2025)
- [Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection](https://mlsanity.com/paper/2504.14250/) (2025)
- [Normality Calibration in Semi-supervised Graph Anomaly Detection](https://mlsanity.com/paper/2510.02014/) (2025)
- [TA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detection](https://mlsanity.com/paper/2603.09349/) (2026)
- [When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection](https://mlsanity.com/paper/2605.10242/) (2026)
- [Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection](https://mlsanity.com/paper/2605.11749/) (2026)
- [A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction](https://mlsanity.com/paper/2606.12673/) (2026)
- [RagGAD: Rationale-Aware Conditional Gaussian Mixture Normalizing Flow for Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2608.16018/) (2026)
- [Multi-Modal Anomaly Detection: A Survey](https://mlsanity.com/paper/2608.24937/) (2026)
- [DiFA: Dual Evidence Fusion and Aggregation for Token-Level Text Anomaly Detection](https://mlsanity.com/paper/2609.22136/) (2026)
- [The Selection Rule Decides the Winner: A Pre-Registered Audit of Open-Set Graph Anomaly Detection](https://mlsanity.com/paper/2609.33370/) (2026)
- [MAADBench: The Refreshable Paradigm for Anomaly Detection in Multi-Agent Systems](https://mlsanity.com/paper/2609.36556/) (2026)

## Abstract (arXiv)

This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph. We reveal that having access to the normal nodes, even just a small percentage of normal nodes, helps enhance the detection performance of existing unsupervised GAD methods when they are adapted to the semi-supervised setting. However, their utilization of these normal nodes is limited. In this paper, we propose a novel Generative GAD approach (namely GGAD) for the semi-supervised scenario to better exploit the normal nodes. The key idea is to generate pseudo anomaly nodes, referred to as 'outlier nodes', for providing effective negative node samples in training a discriminative one-class classifier. The main challenge here lies in the lack of ground truth information about real anomaly nodes. To address this challenge, GGAD is designed to leverage two important priors about the anomaly nodes -- asymmetric local affinity and egocentric closeness -- to generate reliable outlier nodes that assimilate anomaly nodes in both graph structure and feature representations. Comprehensive experiments on six real-world GAD datasets are performed to establish a benchmark for semi-supervised GAD and show that GGAD substantially outperforms state-of-the-art unsupervised and semi-supervised GAD methods with varying numbers of training normal nodes. Code will be made available at https://github.com/mala-lab/GGAD.