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Deep Graph Anomaly Detection (DGAD): A Survey and New Perspectives

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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 newest 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.



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 Problems in our survey paper.



Category of Deep Graph Anomaly Detection


GNN Backbone Design


 


Discriminative GNNs


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]
    

    • 



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]
    

    • 



Generative GNNs


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]
    

    • 



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 Arxiv, 2024. [paper][code]
    

    • 



Proxy Task Design


 


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]
    

    • 



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]
    

    • 



Graph Representation Distillati


    

  • 

    [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]
    

    • 



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]
    

    • 



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]
    

    • 



Graph Anomaly Measure


 


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]
    

    • 



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]
    

    • 



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]
    

    • 



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]
    

    • 



Graph Anomaly Detection Related Survey


    

  • 

    [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]
    

    • 



Anomaly Detection Related Survey


    

  • 

    [Pang2020] Deep Learning for Anomaly Detection: A Review in CSUR, 2020. [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]
    

    • 



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