Awesome Graph Anomaly Detection

Collections for state-of-the-art (SOTA), novel awesome graph anomaly detecion methods (papers, codes and datasets)

We are looking forward for other participants to share their papers and codes. If interested, please contanct jingcan_duan@163.com or jinhu@nudt.edu.cn.

Table of Contents

• Surveys
• Papers and Codes
  • Single-View Static Graph
  • Multi-View Static Graph
  • Temporal Graph
  • Others
• Benchmark Datasets
  • Injected Datasets
  • Real-world Anomaly Datasets
• Other Related Awesome Repository

---

<span id="jump1">Important Survey and Benchmark Papers</span>

1. TKDE 2021: A Comprehensive Survey on Graph Anomaly Detection with Deep Learning [Paper]
2. NeurIPS 2022: BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs [Paper] [Code]

---

<span id="jump2">Papers and Codes</span>

<span id="jump21">Single-View Static Graph</span>

Papers focus on node-level anomaly detection and work on single-view static graph datasets.

<span>Traditional Methods</span>

1. SIGMOD 2000: LOF: Identifying Density-based Local Outliers [Paper][Code]
2. KDD 2007: SCAN: a Structural Clustering Algorithm for Networks [Paper] [Code]
3. SDM 2016: Scalable Anomaly Ranking of Attributed Neighborhoods [Paper][Code]
4. IJCAI 2017: Radar: Residual Analysis for Anomaly Detection in Attributed Networks [Paper] [Code]
5. IJCAI 2018: ANOMALOUS: A Joint Modeling Approach for Anomaly Detection on Attributed Networks [Paper] [Code]

<span>Deep Methods</span>

<span>Reconstruction</span>

1. SDM 2019: Deep Anomaly Detection on Attributed Networks [Paper] [Code]
2. DSAA 2021: ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks [Paper][Code]
3. TKDE 2021: Hybrid-order Anomaly Detection on Attributed Networks [Paper][Code]
4. WSDM 2022: ComGA: Community-Aware Attributed Graph Anomaly Detection [Paper][Code]
5. ICDE 2023: Unsupervised Graph Outlier Detection: Problem, Revisit, New Insight, and Superior Method [Paper] [Code]
6. WSDM 2024: GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction [Paper] [Code]

<span>Reinforcement Learning</span>

1. WSDM 2019: Interactive Anomaly Detection on Attributed Networks [Paper][Code]
2. CIKM 2021: Towards Anomaly-resistant Graph Neural Networks via Reinforcement Learning [Paper]
3. ICDM 2023: Reinforcement Neighborhood Selection for Unsupervised Graph [Paper][Code]

<span>Generative Adversarial Network</span>

1. IJCAI 2020: Inductive Anomaly Detection on Attributed Networks [Paper]
2. CIKM 2020: Generative Adversarial Attributed Network Anomaly Detection [Paper]

<span>Filter</span>

1. IJCAI 2022: Can Abnormality be Detected by Graph Neural Networks? [Paper] [Code]
2. ICML 2022: Rethinking Graph Neural Networks for Anomaly Detection [Paper] [Code]
3. CIKM 2023: SplitGNN: Spectral Graph Neural Network for Fraud Detection against Heterophily [Paper][Code]
4. PR 2024: Graph Fairing Convolutional Networks for Anomaly Detection [Paper] [Code]

<span>One-class SVM</span>

1. CIKM 2021: Subtractive Aggregation for Attributed Network Anomaly Detection [Paper]
2. NCA 2021: One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks [Paper] [Code]

<span>Meta Learning</span>

1. WWW 2021: Few-shot Network Anomaly Detection via Cross-network Meta-learning [Paper] [Code]
2. Arxiv 2023: MetaGAD: Learning to Meta-Transfer for Few-shot Graph Anomaly Detection [Paper]

<span>Contrastive Learning</span>

1. TNNLS 2021: Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [Paper] [Code]

2. CIKM 2021: ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning [Paper] [Code]

3. AAAI 2023: Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View [Paper] [Code]

4. ACM MM 2023: Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning [Paper] [Code]

5. ICDM 2023: PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection [Paper][Code]

6. CIKM 2023: Learning Node Abnormality with Weak Supervision [Paper]

7. SDM 2023: Abnormal Event Detection via Hypergraph Contrastive Learning [Paper][Code]

8. IS 2023: Fraud Detection on Multi-relation Graphs via Imbalanced and Interactive Learning [Paper]

<span>Hybrid Methods</span>

1. TKDE 2021: Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection [Paper] [Code]
2. IJCAI 2022: Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks [Paper] [Code]
3. TKDE 2023: Counterfactual Graph Learning for Anomaly Detection on Attributed Networks [Paper]
4. TNNLS 2023: ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness [Paper] [Code]

<span>Other Self-Supervised Learning</span>

1. Arxiv 2021: Hop-count Based Self-supervised Anomaly Detection on Attributed Networks [Paper] [Code]
2. TII 2023: CaCo: Attributed Network Anomaly Detection via Canonical Correlation Analysis [Paper][Code]

<span>Attention</span>

1. ICDM 2023: Dynamic Relation-Attentive Graph Neural