Awesome Graph Anomaly Detection

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

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

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Important Survey Papers

1. A Comprehensive Survey on Graph Anomaly Detection with Deep Learning [Paper]

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Papers and Codes

Single-View Static Graph

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

Tradition Methods

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]

Deep Methods

Reconstruction

1. SDM 2019: Deep Anomaly Detection on Attributed Networks [Paper] [code]
2. TKDE 2021: Hybrid-order Anomaly Detection on Attributed Networks [Paper] [code]
3. WSDM 2022: ComGA: Community-Aware Attributed Graph Anomaly Detection [Paper] [code]

Reinforcement Learning

1. WSDM 2019: Interactive Anomaly Detection on Attributed Networks [Paper] [code]
2. CIKM 2021: Towards Anomaly-resistant Graph Neural Networks via Reinforcement Learning [Paper]

Generative Adversarial Network

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

Filter

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]

One-class SVM

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]

Meta Learning

1. WWW 2021: Few-shot Network Anomaly Detection via Cross-network Meta-learning [Paper] [code]

Contrastive Learning

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. TKDE 2021: Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection [Paper] [code]
4. IJCAI 2022: Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks [Paper] [code]

Other Self-Supervised Learning

1. WSDM 2022: Hop-count Based Self-supervised Anomaly Detection on Attributed Networks [Paper] [code]

Others

1. AAAI 2022: LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks [Paper] [code]

Multi-View Static Graph

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

Reconstruction

1. TKDE 2022: A Deep Multi-View Framework for Anomaly Detection on Attributed Networks [Paper]

Temporal Graph

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

Others

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

1. WSDM 2022: Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation [Paper] [code]

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Benchmark Datasets

Single-View Static Graph

Injected Datasets

By way of injection, adding anomalous nodes to datasets that do not have anomalies before. These anomalous nodes consist of feature anomalies and structure anomalies. The total number of anomalies are shown in the 5th column of table.

Dataset	Nodes	Edges	Features	Anomalies	URL
BlogCatalog	5196	171743	8189	300	[BlogCatalog]
Flickr	7575	239738	12407	450	[Flickr]
ACM	16484	71980	8337	600	[ACM]
Cora	2708	5429	1433	150	[Cora]
Citeseer	3327	4732	3703	150	[Citeseer]
Pubmed	19717	44338	500	600	[Pubmed]

Real-world Anomaly Datasets

These datasets are born with anomalous nodes.

Datasets	Nodes	Edges	Features	Anomalies	URL
Amazon	1418	3695	21	28	[Amazon]
Enron	13533	176987	20	5	[Enron]
YelpChi	45954	3846979	32	6677	[YelpChi]
T-Finance	39357	21222543	10	1803	[T-Finance]
T-Social	5781065	73105508	10	174010	[T-Social]
Elliptic	46564	73248	93	4,545	[Elliptic]

Other Related Awesome Repository

awesome-multi-view-clustering

Awesome Deep Graph Clustering