Deep Graph Anomaly Detection: A Survey and New Perspectives

A professionally curated list of awesome resources (paper, code, data, etc.) on Deep Graph Anomaly Detection (DGAD), which is the first work to comprehensively and systematically summarize the recent advances of deep graph anomaly detection from the methodology design to the best of our knowledge.

We will continue to update this list with the latest resources. If you find any missed resources (paper/code) or errors, please feel free to open an issue or make a pull request.

Survey Paper

Deep Graph Anomaly Detection: A Survey and New Perspectives

Hezhe Qiao, Hanghang Tong, Bo An, Irwin King, Charu Aggarwal, Guansong Pang.

If you find this repository helpful for your work, please kindly cite our paper.

@article{qiao2024deep,
  title={Deep Graph Anomaly Detection: A Survey and New Perspectives},
  author={Qiao, Hezhe and Tong, Hanghang and An, Bo and King, Irwin and Aggarwal, Charu and Pang, Guansong},
  journal={arXiv preprint arXiv:2409.09957},
  year={2024}
}

Feel free to point out any mistakes and welcome to provide relevant papers.

Taxonomy of Deep Graph Anomaly Detection

Outline

The outline corresponds to the taxonomy of methods in our survey paper.

• 1. GNN Backbone Design
  • 1.1 Discriminative GNNs
    • 1.1.1 Aggregation Mechanism
    • 1.1.2 Feature Transformation
  • 1.2 Generative GNNs
    • 1.2.1 Feature Interpolation
    • 1.2.2 Noise Perturbation
• 2. Proxy Task Design
  • 2.1 Graph Reconstruction
  • 2.2 Graph Contrastive Learning
  • 2.3 Graph Representation Distillati
  • 2.4 Adversarial Graph Learning
  • 2.5 Score Prediction
• 3. Graph Anomaly Measures
  • 3.1 One-class Classification Measure
  • 3.2 Community Adherence
  • 3.3 Local Affinity
  • 3.4 Graph Isolation
• 4. Graph Anomaly Detection Related Survey
• 5. Anomaly Detection Related Survey
• 6. Quantitative Comparison
• 7. Datasets

Categories of Deep Graph Anomaly Detection

1. GNN Backbone Design

1.1 Discriminative GNNs

1.1.1 Aggregation Mechanism

• [Dou2020] Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters in CIKM, 2020. [paper][code]

• [Liu2020] Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection in SIGIR, 2020. [paper][code]

• [Liu2021] Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection in WWW, 2021.[paper][code]

• [Zhang2021] FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance in ICDM, 2021. [paper][code]

• [Zhang2022] Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection in NeurIPS, 2022. [paper]

• [Qin2022] Explainable Graph-based Fraud Detection via Neural Meta-graph Search in CIKM, 2022. [paper][code]

• [Dong2022] Bi-Level Selection via Meta Gradient for Graph-based Fraud Detection in DASFAA, 2022. [paper]

• [Shi2022] H2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic Connections in WebConf, 2022. [paper]

• [Gao2023] Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum in WebConf, 2023. [paper][code]

• [Ma2023] Towards Graph-level Anomaly Detection via Deep Evolutionary Mapping in KDD, 2023. [paper][code]

• [Chang2024] Multitask Active Learning for Graph Anomaly Detection in Arxiv, 2024. [paper][code]

• [Zhang2024] Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection in Arxiv, 2024. [paper]

• [Chen2024] Boosting Graph Anomaly Detection with Adaptive Message Passing in ICLR, 2024. [paper]

• [Zhuo2023] Partitioning Message Passing for Graph Fraud Detection in ICLR, 2024. [paper][code]

• [Gao2024] Graph Anomaly Detection with Bi-level Optimization in WebConf, 2024. [paper][code]

• [Guo2024] Graph Local Homophily Network for Anomaly Detection in CIKM, 2024. [paper]

• [Ma2024] Revisiting Score Propagation in Graph Out-of-Distribution Detection in NeurIPS, 2024. [paper][code]

• [Lin2024] UniGAD: Unifying Multi-level Graph Anomaly Detection in NeurIPS, 2024. [paper][code]

1.1.2 Feature Transformation

• [Chai2022] Can Abnormality be Detected by Graph Neural Networks? in IJCAI, 2022. [paper][code]

• [Tang2022] Rethinking Graph Neural Networks for Anomaly Detection in ICML, 2022.[paper][code]

• [Gao2023] Alleviating Structural Distribution Shift in Graph Anomaly Detection in WSDM, 2023.[paper][code]

• [Dong2023] Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection in ICLR, 2024.[paper][code]

• [Dong2024] SmoothGNN: Smoothing-based GNN for Unsupervised Node Anomaly Detection in Arxiv, 2024. [paper]

1.2 Generative GNNs

1.2.1 Feature Interpolation

• [Zhao2021] GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks in WSDM, 2021 [paper][code]

• [Han2022] G-Mixup: Graph Data Augmentation for Graph Classification in ICML, 2022 [paper][code]

• [Park2022] GRAPHENS:Neighbor-aware Ego Network Synthesis for Class-imbalance Node Classification in ICLR, 2022. [paper][code]

• [Liu2022] DAGAD: Data Augmentation for Graph Anomaly Detection in ICDM, 2022. [paper][code]

• [Lou2023] GADY Unsupervised Anomaly Detection on Dynamic Graphs in Arxiv, 2023. [paper]

• [Meng2023] Generative Graph Augmentation for Minority Class in Fraud Detection in CIKM, 2023. [paper]

• [Zhou2023] Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation in TKDE, 2023. [paper][code]

• [Liu2024] Class-Imbalanced Graph Learning without Class Rebalancing in ICML, 2024. [paper][code]

• [Chen2024] Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision in ICLR, 2024. [paper][code]

• [Zhou2024] Graph Anomaly Detection with Adaptive Node Mixup in CIKM, 2024. [paper]

• [Kim2024] ANOMIX: A Simple yet Effective Hard Negative Generation via Mixing for Graph Anomaly Detection in Arxiv, 2024. [paper][code]

1.2.2 Noise Perturbation

• [Cai2023] Self-Discriminative Modeling for Anomalous Graph Detection in Arxiv, 2023. [paper]

• [Liu2023] GODM Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models in Arxiv, 2023. [paper][code]

• [Lou2023] GADY: Unsupervised Anomaly Detection on Dynamic Graphs in Arxiv, 2023. [paper][code]

• [Ma2024] Graph Anomaly Detection with Few Labels: A Data-Centric Approach in KDD, 2024. [paper]

• [Qiao2024] Generative Semi-supervised Graph Anomaly Detection in NeurIPS, 2024. [paper][code]

• [Li2024] DiffGAD: A Diffusion-based unsupervised graph anomaly detector in Arxiv, 2024. [paper][code]

2. Proxy Task Design

2.1 Graph Reconstruction

• [Yu2018] NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks in KDD, 2018. [paper][code]

• [Ding2019] Deep Anomaly Detection on Attributed Networks in SDM, 2019. [paper][code]

• [Fan2020] ANOMALYDAE: Dual Autoencoder for Anomaly Detection on Attribute Networks in ICASSP, 2020. [paper][code]

• [Bandyopadhyay2020] Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding in WSDM, 2020. [paper][code]

• [Pei2022] ResGCN Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks in Machine Learning, 2022. [paper][code]

• [Liu2022] Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information in Arxiv, 2022. [paper][code]

• [Chen2022] AnomMAN: Detect Anomaly on Multi-view Attributed Networks in Information Sciences, 2022.[paper]

• [Peng2022] A Deep Multi-View Framework for Anomaly Detection on Attributed Networks in TKDE, 2022. [paper]

• [Luo2022] ComGA: Community-Aware Attributed Graph Anomaly Detection in WSDM, 2022. [paper][code]

• [Zhang2022] Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks in IJCAI, 2022. [paper][code]

• [Huang2022] Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method in Arxiv, 2022. [paper][code]

• [Niu2023] Graph-level Anomaly Detection via Hierarchical Memory Networks in ECML PKDD, 2023. [paper][code]

• [Huang2023] Hybrid-Order Anomaly Detection on Attributed Networks in TKDE, 2023 [paper][code]

• [Mesgaran2023] A graph encoder–decoder network for unsupervised anomaly detection in Arxiv, 2023. [paper]

• [Kim2023] Label-based Graph Augmentation with Metapath for Graph Anomaly Detection in Arxiv, 2023. [paper][code]

• [Roy2024] GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction in WSDM, 2024. [paper][code]

• [He2024] ADA-GAD:Anomaly-Denoised Autoencoders for Graph Anomaly Detection in AAAI, 2024. [paper][code]

• [Liu2024] STRIPE Spatial-temporal Memories Enhanced Graph Autoencoder for Anomaly Detection in Dynamic Graphs in Arxiv, 2024. [paper]

• [Zou2024] A Structural Information Guided Hierarchical Reconstruction for Graph Anomaly Detection in CIKM, 2024. [paper]

• [Kim2024] Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy in Arxiv, 2024. [paper]

2.2 Graph Contrastive Learning

• [Jin2021] ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning in CIKM, 2021. [paper]

• [Zheng2021] Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection in TKDE, 2021. [paper][code]

• [Liu2021] Anomaly Detection in Dynamic Graphs via Transformer in TKDE, 2021. [paper][code]

• [Liu2021] CoLA Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning in TNNLS, 2021. [paper][code]

• [Xu2022] CONDA Contrastive Attributed Network Anomaly Detection with Data Augmentation in PAKDD, 2022. [paper][code]

• [Wang2021] Decoupling Representation Learning and Classification for GNN-based Anomaly Detection in SIGIR, 2021. [paper][code]

• [Chen2022] GCCAD:Graph Contrastive Coding for Anomaly Detection in TKDE, 2022. [paper][code]

• [Wang2022] Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment in AAAI, 2022. [paper][code]

• [Zhang2022] Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks in IJCAI, 2022. [paper][code]

• [Xu2023] Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection in Arxiv, 2023. [paper]

• [Duan2023] ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness in TNNLS, 2023. [paper][code]

• [Liu2023] BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection in Arxiv, 2023. [paper]

• [Ding2023] GOOD-D:On Unsupervised Graph Out-Of-Distribution Detection in WSDM, 2023. [paper][code]

• [Duan2023] GRADATE:Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View in AAAI, 2023. [paper][code]

• [Singh2023] GraphFC:Customs Fraud Detection with Label Scarcity in Arxiv, 2023. [paper][code]

• [Liu2023] Revisiting Graph Contrastive Learning for Anomaly Detection in Arxiv, 2023. [paper][code]

• [Lin2023] Multi-representations Space Separation based Graph-level Anomaly-aware Detection in SSDBM, 2023. [paper][code]

• [Liu2023] Towards Self-Interpretable Graph-Level Anomaly Detection in NeurIPS, 2023. [paper][code]

• [Zhou2023] Learning Node Abnormality with Weak Supervision in CIKM, 2023. [paper]

• [Kong2024] Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning in TNNLS, 2024. [paper]

• [Chen2024] Towards Cross-domain Few-shot Graph Anomaly Detection in ICDM, 2024. [paper]

• [Niu2024] Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts in Arxiv, 2024. [paper][code]

• [Cheng2024] Graph Pre-Training Models Are Strong Anomaly Detectors in Arxiv, 2024. [paper]

2.3 Graph Representation Distillation

• [Ma2020] Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation in CIKM, 2020. [paper][code]

• [Lin2023] Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model in Arxiv, 2023. [paper]

• [Cai2024] FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework in Arxiv, 2024. [paper]

2.4 Adversarial Graph Learning

• [Chen2020] Generative Adversarial Attributed Network Anomaly Detection in CIKM, 2020. [paper][code]

• [Ding2021] Inductive Anomaly Detection on Attributed Networks in IJCAI, 2021. [paper][code]

• [Xiao2023] Counterfactual Graph Learning for Anomaly Detection on Attributed Networks in TKDE, 2023. [paper][code]

• [Meng2023] Generative Graph Augmentation for Minority Class in Fraud Detection in Arxiv, 2023. [paper][code]

2.5 Score Prediction

• [Pang2019] DevNet Deep Anomaly Detection with Deviation Networks in KDD, 2019. [paper][code]

• [Ding2021] Few-shot Network Anomaly Detection via Cross-network in WebConf, 2021. [paper][code]

• [Tian2023] SAD:Semi-Supervised Anomaly Detection on Dynamic Graphs in IJCAI, 2023. [paper][code]

• [Zhou2023] Learning Node Abnormality with Weak Supervision in CIKM, 2023. [paper]

• [Xu2024] MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection in DSAA, 2024. [paper][code]

3. Graph Anomaly Measures

3.1 One-class Classification Measure

• [Teng2018] Deep into Hypersphere: Robust and Unsupervised Anomaly Discovery in Dynamic Networks in IJCAI, 2018. [paper][code]

• [Wang2021] One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks in * Neural Comput & Applic*, 2021. [paper][code]

• [Zhou2021] Subtractive Aggregation for Attributed Network Anomaly Detection in CIKM, 2021. [paper][code]

• [Li2023] HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks in DSAA, 2023. [paper][code]

• [Zhang2023] Deep Graph-level Orthogonal Hypersphere Compression for Anomaly Detection in ICLR, 2024. [paper][code]

3.2 Community Adherence

• [Yu2018] NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks in KDD, 2018. [paper][code]

• [Zhou2022] Unseen Anomaly Detection on Networks via Multi-Hypersphere Learning in SDM, 2022. [paper][code]

3.3 Local Affinity

• [Kim2023] Class Label-aware Graph Anomaly Detection in CIKM, 2023. [paper][code]

• [Pan2023] PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection in ICDM, 2023. [paper][code]

• [Qiao2023] Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection in NeurIPS, 2023. [paper][code]

• [Liu2024] ARC: A Generalist Graph Anomaly Detector with In-Context Learning in Arxiv, 2024. [paper]

• [Wang2024] Context Correlation Discrepancy Analysis for Graph Anomaly Detection in TKDE, 2024. [paper]

3.4 Graph Isolation

• [Xu2023] Deep Isolation Forest for Anomaly Detection in TKDE, 2023. [paper][code]

• [Zhuang2023] Subgraph Centralization: A Necessary Step for Graph Anomaly Detection in SDM,2023. [paper][code]

4. Related Surveys on Graph Anomaly Detection

• [Ma2021] A Comprehensive Survey on Graph Anomaly Detection with Deep Learning in TKDE, 2021. [paper]

• [Liu2022] BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs in NeurIPS, 2022. [paper][code]

• [Tang2023] GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection in NeurIPS, 2023. [paper][code]

• [Liu2023] A survey of imbalanced learning on graphs: Problems, techniques, and future direction in Arxiv, 2024. [paper]

• [Ekle2024] Anomaly Detection in Dynamic Graphs: A Comprehensive Survey in Arxiv, 2024. [paper]

• [Wang2024] Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection:A Benchmark in Arxiv, 2024. [paper][code]

5. Related Surveys on Anomaly Detection

• [Pang2021] Deep Learning for Anomaly Detection: A Review in CSUR, 2021. [paper]

• [Jiang2023] Weakly Supervised Anomaly Detection: A Survey in Arxiv, 2023. [paper][code]

• [Xu2024] Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey in Arxiv, 2024. [paper]

6. Quantitative Comparison

Quantitative comparison of node-level anomaly detection on datasets with manually injected (synthetic) anomalies

Metric	Setting	Cora	Citeseer	ACM	BlogCatalog	Flicker	Pubmed	Facebook	Reddit	Weibo
AUROC	DOMINANT [Ding2019]	0.815	0.825	0.760	0.746	0.744	0.808	0.554	0.560	0.850
AUROC	CoLA [Liu2021]	0.878	0.896	0.823	0.785	0.751	0.951	/	0.603	/
AUROC	SL-GAD [Zheng2021]	0.913	0.913	0.853	0.818	0.796	0.967	/	0.567	/
AUROC	CONAD [Xu2022]	0.788	/	/	/	/	/	0.863	0.561	0.854
AUROC	AEGIS [Ding2021]	/	/	/	0.743	0.738	0.773	/	/	/
AUROC	OCGNN [Wang2021]	0.881	0.856	/	/	/	0.747	0.793	/	/
AUROC	ComGA [Luo2022]	0.884	0.916	0.849	0.814	0.799	0.922	0.659	/	/
AUROC	AAGNN [Zhou2021]	/	/	/	0.818	0.829	0.856	/	/	0.925
AUROC	HCM-A [Huang2022]	/	/	0.761	0.798	0.792	/	/	/	/
AUROC	GAAN [Chen2020]	0.742	/	0.877	0.765	0.753	/	/	0.554	0.925
AUROC	AnomalyDAE [Fan2020]	0.762	0.727	0.778	0.783	0.751	0.810	/	0.557	0.915
AUROC	GAD-NR [Roy2023]	0.835	/	/	/	/	/	/	/	0.623
AUROC	TAM [Qiao2023]	/	/	0.887	0.824	/	/	0.914	0.602	/
AURPC	DOMINANT [Ding2019]	0.200	/	/	0.338	0.324	0.299	/	0.037	/
AURPC	CoLA [Liu2021]	/	/	0.323	0.327	/	/	0.211	0.044	/
AURPC	SL-GAD [Zheng2021]	/	/	/	0.388	0.378	/	0.131	0.041	/
AURPC	CONAD [Xu2022]	/	/	/	/	/	/	/	0.037	/
AURPC	AEGIS [Ding2021]	/	/	/	0.339	0.324	0.373	/	/	/
AURPC	OCGNN [Wang2021]	/	/	/	/	/	/	/	/	/
AURPC	ComGA [Luo2022]	/	/	/	/	/	/	/	/	/
AURPC	AAGNN [Zhou2021]	/	/	/	0.435	0.421	0.428	/	/	/
AURPC	HCM-A [Huang2022]	/	/	/	/	/	/	/	/	/
AURPC	GAAN [Chen2020]	/	/	/	0.338	0.324	0.337	/	0.037	/
AURPC	AnomalyDAE [Fan2020]	0.183	/	/	/	/	/	/	/	/
AURPC	GAD-NR [Roy2023]	/	/	/	/	/	/	/	/	/
AURPC	TAM [Qiao2023]	/	/	0.512	0.418	/	/	0.223	0.044	/

Quantitative comparison of node-level anomaly detection on datasets with genuine anomalies

Metric	Setting	Amazon	YelpChi	T-Finance	Question	Elliptic	Reddit	Tolokers	Weibo	DGraph	T-Social	Photo	CS
AUROC	Unsupervised	DOMINANT [Ding2019]	0.694	0.539	0.538	/	0.296	0.556	/	/	0.574	/	0.514
AUROC	Unsupervised	CoLA [Liu2021]	0.261	0.480	0.483	/	/	0.603	/	/	/	/	/
AUROC	Unsupervised	CLAD [Kim2023]	0.203	0.476	0.139	0.621	0.419	0.578	0.406	/	/	/	/
AUROC	Unsupervised	GRADATE [Duan2023]	0.478	0.492	0.406	0.554	/	0.526	0.537	/	/	/	/
AUROC	Unsupervised	GAD-NR [Roy2023]	0.260	0.470	0.579	0.587	0.400	0.553	0.576	/	/	/	/
AUROC	Unsupervised	Prem [Pan2023]	0.278	0.490	0.448	0.603	0.497	0.551	0.565	/	/	/	/
AUROC	Unsupervised	TAM [Qiao2023]	0.802	0.548	0.690	0.504	/	0.572	0.469	/	/	/	/
AUROC	Unsupervised	SmoothGNN [Dong2024]	0.840	0.575	0.755	0.644	0.572	0.594	0.687	/	0.649	0.703	/
AUROC	Semi-supervised	GGAD [Qiao2024]	0.944	/	0.823	/	0.729	/	/	/	0.594	/	0.648
AUROC	Supervised	BWGNN [Tang2022]	0.980	0.849	0.961	0.718	0.852	0.654	0.804	0.973	0.763	0.920	/
AUROC	Supervised	DCI [Wang2021]	0.946	0.778	0.868	0.692	0.828	0.665	0.755	0.942	0.747	0.808	/
AUROC	Supervised	AMNet [Chai2022]	0.970	0.826	0.937	0.681	0.773	0.684	0.768	0.953	0.731	0.536	/
AUROC	Supervised	GHRN [Gao2023]	0.981	0.853	0.96	0.718	0.854	0.660	0.804	0.967	0.761	0.790	/
AUROC	Supervised	NGS [Qin2022]	0.973	0.921	/	/	/	/	/	/	/	/	/
AUROC	Supervised	PCGNN [Liu2021]	0.973	0.797	0.933	0.699	0.858	0.532	0.728	0.902	0.720	0.692	/
AUROC	Supervised	GDN [Gao2023]	0.971	0.903	/	/	/	/	/	/	/	/	/
AUROC	Supervised	DevNet [Pang2019]	/	/	0.654	/	/	/	/	/	/	/	0.599
AUROC	Supervised	PReNet [Pang2023]	/	/	0.892	/	/	/	/	/	/	/	0.698
AUROC	Supervised	NSReg [Wang2023]	/	/	0.929	/	/	/	/	/	/	/	0.908
AUPRC	Unsupervised	DOMINANT [Ding2019]	0.102	0.165	0.047	/	/	0.036	/		0.008	/	0.104
AUPRC	Unsupervised	CoLA [Liu2021]	0.052	0.136	0.041	/	/	0.045	/	/	/	/	0.246
AUPRC	Unsupervised	CLAD [Kim2023]	0.040	0.128	0.025	0.051	0.081	0.050	0.192	/	/	/	/
AUPRC	Unsupervised	GRADATE [Duan2023]	0.063	0.145	0.038	0.035	/	0.039	0.236	/	/	/	/
AUPRC	Unsupervised	GADNR [Roy2023]	0.042	0.139	0.054	0.057	0.077	0.037	0.299	/	/	/	/
AUPRC	Unsupervised	Prem [Pan2023]	0.074	0.137	0.039	0.043	0.090	0.041	0.259	/	/	/	/
AUPRC	Unsupervised	TAM [Qiao2023]	0.332	0.173	0.128	0.039	/	0.042	0.196	/	/	/	/
AUPRC	Unsupervised	SmoothGNN [Dong2024]	0.395	0.182	0.140	0.059	0.116	0.043	0.351	/	0.019	0.063	/
AUPRC	Semi-supervised	GGAD [Qiao2024]	0.792	/	0.183	/	0.243	0.061	/	/	0.008	/	0.144
AUPRC	Supervised	BWGNN [Tang2022]	0.891	0.551	0.866	0.167	0.260	0.069	0.497	0.930	0.040	0.549	/
AUPRC	Supervised	DCI [Wang2021]	0.815	0.395	0.626	0.141	0.254	0.061	0.399	0.896	0.036	0.138	/
AUPRC	Supervised	AMNet [Chai2022]	0.873	0.488	0.743	0.146	0.147	0.073	0.432	0.897	0.028	0.031	/
AUPRC	Supervised	GHRN [Gao2023]	0.895	0.566	0.866	0.167	0.277	0.072	0.499	0.918	0.04	0.163	/
AUPRC	Supervised	NGS [Qin2022]	/	/	/	/	/	/	/	/	/	/	/
AUPRC	Supervised	PCGNN [Liu2021]	0.878	0.437	0.698	0.144	0.356	0.042	0.381	0.819	0.028	0.087	/
AUPRC	Supervised	DevNet [Pang2019]	/	/	0.323	/	/	/	/	/	/	/	0.468
AUPRC	Supervised	PReNet [Pang2023]}	/	/	0.571	/	/	/	/	/	/	/	0.460
AUPRC	Supervised	NSReg [Wang2023]	/	/	0.757	/	/	/	/	/	/	/	0.836

Quantitative comparison of graph-level anomaly detection

Metric	Methods	PROTEINS-F	ENZYMES	AIDS	DHFR	BZR	COX2	DD	NCI1	IMDB	COLLAB	HSE	MMP	P53	TraceLog	FlowGraph
AUROC	GlocalKD [Ma2022]	0.773	0.613	0.932	0.567	0.694	0.593	0.801	0.684	0.521	0.674	0.593	0.675	0.640	/	/
AUROC	OCGIN [Zhao2023]	0.708	0.587	0.781	0.492	0.659	0.535	0.722	0.719	0.601	/	/	/	/	/	/
AUROC	SIGNET [Liu2024]	0.752	0.629	0.972	0.740	0.814	0.714	0.727	0.748	0.664	/	/	/	/	/	/
AUROC	OCGTL [Qiu2022]	0.765	0.620	0.994	0.599	0.639	0.552	0.794	0.734	0.640	/	/	/	/	/	/
AUROC	OCGCN [Wang2021]	0.718	0.613	0.664	0.495	0.658	0.628	0.605	0.627	0.536	/	0.388	0.457	0.483	/	/
AUROC	HimNet [Niu2023]	0.772	0.589	0.997	0.701	0.703	0.637	0.806	0.686	0.553	0.683	0.613	0.703	0.646	/	/
AUROC	GLADST [Lin2023]	/	0.694	0.976	0.773	0.810	0.630	/	0.681	/	0.776	0.547	0.685	0.688	/	/
AUROC	DIF [Xu2023]	/	/	/	/	/	/	/	/	/	/	0.737	0.715	0.680	/	/
AUROC	HRGCN [Li2023]	/	/	/	/	/	/	/	/	/	/	/	/	/	0.864	1.000

7. Datasets

Dataset	# Nodes	# Edges	# Attributes	Size	Anomaly	Anomaly Type	Domain	Download Link
Cora	2,708	5,429	1,433	Small	5.5%	Injected	Citation Networks	[Link]
Citersee	3,327	4,732	3,703	Small	4.5%	Injected	Citation Networks	[Link]
ACM	16,484	71,980	8,337	Medium	3.6%	Injected	Citation Networks	[Link]
BlogCatalog	5,196	171,743	8,189	Small	5.8%	Injected	Social Networks	[Link]
Flickr	7,575	239,738	12,407	Medium	5.2%	Injected	Social Networks	[Link]
OGB-arXiv	169,343	1,166,243	128	Large	3.5%	Injected	Citation Networks	[Link]
Amazon	11,944	4,398,392	25	Large	9.5%	Genuine	Transaction Record	[Link]
YelpChi	45,954	3,846,979	32	Large	14.5%	Genuine	Reviewer Interaction	[Link]
T-Finance	39,357	21,222,543	10	Large	4.6%	Genuine	Transaction Record	[Link]
T-Social	5,781,065	73,105,508	10	Large	3.0%	Genuine	Social Network	[Link]
Weibo	8,405	407,963	400	Small	10.3%	Genuine	Under Same Hashtag	[Link]
DGraph	3,700,550	4,300,999	17	Large	1.3%	Genuine	Loan Guarantor	[Link]
Elliptic	203,769	234,355	166	Large	9.8%	Genuine	Payment Flow	[Link]
Tolokers	11,758	519,000	10	Medium	21.8%	Genuine	Work Collaboration	[Link]
Questions	48,921	153,540	301	Medium	3.0%	Genuine	Question Answering	[Link]
Disney	124	335	28	Small	4.8%	Genuine	Co-purchase	[Link]
Books	1,418	3,695	21	Small	2.0%	Genuine	Co-purchase	[Link]
Enron	13,533	176,987	18	Medium	0.4%	Genuine	Email network	[Link]
Reddit	10,984	168,016	64	Medium	3.3%	Genuine	User-subreddit	[Link]

Dataset	# Graphs	# Avg. Nodes	# Edges	Anomaly	Domain	Homo./Heter.	Download Link
KKI	83	190	237.4	44.6%	Bioinformatics	Homo.	[Link]
OHSU	79	82.01	199.66	44.3%	Bioinformatics	Homo.	[Link]
MUTAG	188	17.93	19.79	33.5%	Molecules	Homo.	[Link]
PROTEINSfull	1,113	39.06	72.82	40.4%	Bioinformatics	Homo.	[Link]
ENZYMES	600	32.63	62.14	16.7%	Bioinformatics	Homo.	[Link]
AIDS	2,000	15.69	16.2	20.0%	Chemical Structure	Homo.	[Link]
BZR	405	35.75	38.36	21.0%	Molecules	Homo.	[Link]
COX2	467	41.22	43.45	21.8%	Molecules	Homo.	[Link]
DD	1,178	284.32	715.66	41.3%	Bioinformatics	Homo.	[Link]
NCI1	4,110	29.87	32.3	49.9%	Molecules	Homo.	[Link]
IMDB	1,000	19.77	96.53	50.0%	Social Networks	Homo.	[Link]
REDDIT	2,000	429.63	497.75	50.0%	Social Networks	Homo.	[Link]
HSE	8,417	16.89	17.23	5.2%	Molecules	Homo.	[Link]
MMP	7,558	17.62	17.98	15.6%	Molecules	Homo.	[Link]
p53	8,903	17.92	18.34	6.3%	Molecules	Homo.	[Link]
PPAR-gamma	8,451	17.38	17.72	2.8%	Molecules	Homo.	[Link]
COLLAB	5,000	74.49	2,457.78	15.5%	Social Networks	Homo.	[Link]
Mutagenicit	4,337	30.32	30.77	44.6%	Molecules	Homo.	[Link]
DHFR	756	42.43	44.54	39.0%	Molecules	Homo.	[Link]
TraceLog	132,485	205	224	17.6%	Log Sequences	Heter.	[Link]
FlowGraph	600	8,411	12,730	16.7%	System Flow	Heter.	[Link]