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Rethinking Unsupervised graph anomaly detection With Deep Learning: Residuals and Objectives

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 881-895页

作者： Ma, Xiaoxiao Liu, Fanzhen Wu, Jia Yang, Jian Xue, Shan Sheng, Quan Z. Macquarie Univ Sch Comp Sydney NSW 2109 Australia CSIROs Data61 Sydney NSW 2015 Australia

Anomalies often occur in real-world information networks/graphs, such as malevolent users in online review networks and fake news in social media. When representing such structured network data as graphs, anomalies usually appear as anomalous nodes that exhibit significantly deviated structure patterns, or different attributes, or the both. To date, numerous unsupervised methods have been developed to detect anomalies based on residual analysis, which assumes that anomalies will introduce larger residual errors (i.e., graph reconstruction loss). While these existing works achieved encouraging performance, in this paper, we formally prove that their employed learning objectives, i.e., MSE and cross-entropy losses, encounter significant limitations in learning the major data distributions, particularly for anomaly detection, and through our preliminary study, we reveal that the vanilla residual analysis-based methods cannot effectively investigate the rich graph structure. Upon these discoveries, we propose a novel structure-biased graph anomaly detection framework (SALAD) to attain anomalies' divergent patterns with the assistance of a specially designed node representation augmentation approach. We further present two effective training objectives to empower SALAD to effectively capture the major structure and attribute distributions by emphasizing less on anomalies that introduce higher reconstruction errors under the encoder-decoder framework. The detection performance on eight widely-used datasets demonstrates SALAD's superiority over twelve state-of-the-art baselines. Additional ablation and case studies validate that our data augmentation method and training objectives result in the impressive performance.

关键词： anomaly detection Training Data augmentation Reviews graph neural networks Fraud Web sites Stochastic processes Multimedia communication Matrix decomposition graph anomaly detection data augmentation residual analysis graph neural network unsupervised learning

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Counterfactual Data Augmentation With Denoising Diffusion for graph anomaly detection

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第6期11卷 7555-7567页

作者： Xiao, Chunjing Pang, Shikang Xu, Xovee Li, Xuan Trajcevski, Goce Zhou, Fan Henan Univ Sch Comp & Informat Engn Kaifeng 475004 Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Univ Elect Sci & Technol China Chengdu 610054 Sichuan Peoples R China Sichuan Univ Natl Key Lab Fundamental Sci Synthet Vis Chengdu 610065 Peoples R China Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

A critical aspect of graph neural networks (GNNs) is to enhance the node representations by aggregating node neighborhood information. However, when detecting anomalies, the representations of abnormal nodes are prone to be averaged by normal neighbors, making the learned anomaly representations less distinguishable. To tackle this issue, we propose an unsupervised counterfactual data augmentation method for graph anomaly detection (CAGAD) that introduces a graph pointer neural network as the heterophilic node detector to identify potential anomalies whose neighborhoods are normal-node-dominant. For each identified potential anomaly, we design a graph-specific diffusion model to translate a part of its neighbors, which are probably normal, into anomalous ones. At last, we involve these translated neighbors in GNN neighborhood aggregation to produce counterfactual representations of anomalies. Through aggregating the translated anomalous neighbors, counterfactual representations become more distinguishable and further advocate detection performance. The experimental results on four datasets demonstrate that CAGAD significantly outperforms strong baselines, with an average improvement of 2.35% on F1, 2.53% on AUC-ROC, and 2.79% on AUC-PR.

关键词： Counterfactual data augmentation graph anomaly detection graph neural network (GNN) representation learning

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ARISE: graph anomaly detection on Attributed Networks via Substructure Awareness

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第12期35卷 18172-18185页

作者： Duan, Jingcan Xiao, Bin Wang, Siwei Zhou, Haifang Liu, Xinwang Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China Chongqing Univ Posts & Telecommun Dept Comp Sci & Technol Chongqing 400065 Peoples R China Intelligent Game & Decis Lab Beijing 100071 Peoples R China

Recently, graph anomaly detection on attributed networks has attracted growing attention in data mining and machine learning communities. Apart from attribute anomalies, graph anomaly detection also aims at suspicious topological-abnormal nodes that exhibit collective anomalous behavior. Closely connected uncorrelated node groups form uncommonly dense substructures in the network. However, existing methods overlook that the topology anomaly detection performance can be improved by recognizing such a collective pattern. To this end, we propose a new graph anomaly detection framework on attributed networks via substructure awareness (ARISE). Unlike previous algorithms, we focus on the substructures in the graph to discern abnormalities. Specifically, we establish a region proposal module to discover high-density substructures in the network as suspicious regions. The average node-pair similarity can be regarded as the topology anomaly degree of nodes within substructures. Generally, the lower the similarity, the higher the probability that internal nodes are topology anomalies. To distill better embeddings of node attributes, we further introduce a graph contrastive learning scheme, which observes attribute anomalies in the meantime. In this way, ARISE can detect both topology and attribute anomalies. Ultimately, extensive experiments on benchmark datasets show that ARISE greatly improves detection performance (up to 7.30% AUC and 17.46% AUPRC gains) compared to state-of-the-art attributed networks anomaly detection (ANAD) algorithms.

关键词： Attributed networks graph anomaly detection graph neural networks (GNNs) network substructure

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Context Correlation Discrepancy Analysis for graph anomaly detection

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第1期37卷 174-187页

作者： Wang, Ruidong Xi, Liang Zhang, Fengbin Fan, Haoyi Yu, Xu Liu, Lei Yu, Shui Leung, Victor C. M. Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321000 Peoples R China Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150080 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China China Univ Petr East China Qingdao Inst Software Qingdao 266580 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China Qilu Univ Technol Shandong Acad Sci Key Lab Comp Power Network & Informat Secur Minist Educ Jinan 250014 Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW 2007 Australia Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

In unsupervised graph anomaly detection, existing methods usually focus on detecting outliers by learning local context information of nodes, while often ignoring the importance of global context. However, global context information can provide more comprehensive relationship information between nodes in the network. By considering the structure of the entire network, detection methods are able to identify potential dependencies and interaction patterns between nodes, which is crucial for anomaly detection. Therefore, we propose an innovative graph anomaly detection framework, termed CoCo (Context Correlation Discrepancy Analysis), which detects anomalies by meticulously evaluating variances in correlations. Specifically, CoCo leverages the strengths of Transformers in sequence processing to effectively capture both global and local contextual features of nodes by aggregating neighbor features at various hops. Subsequently, a correlation analysis module is employed to maximize the correlation between local and global contexts of each normal node. Unseen anomalies are ultimately detected by measuring the discrepancy in the correlation of nodes' contextual features. Extensive experiments conducted on six datasets with synthetic outliers and five datasets with organic outliers have demonstrated the significant effectiveness of CoCo compared to existing methods.

关键词： anomaly detection Correlation Feature extraction Computer science Vectors Transformers Software Semantics Reviews Representation learning graph anomaly detection graph neural networks canonical correlation analysis graph embedding

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AnomalGRN: deciphering single-cell gene regulation network with graph anomaly detection

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BMC BIOLOGY 2025年第1期23卷 1-16页

作者： Zhou, Zhecheng Wei, Jinhang Liu, Mingzhe Zhuo, Linlin Fu, Xiangzheng Zou, Quan Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325027 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410012 Peoples R China Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 611730 Peoples R China

BackgroundSingle-cell RNA sequencing (scRNA-seq) is now essential for cellular-level gene expression studies and deciphering complex gene regulatory mechanisms. Deep learning methods, when combined with scRNA-seq technology, transform gene regulation research into graph link prediction tasks. However, these methods struggle to mitigate the impact of noisy data in gene regulatory networks (GRNs) and address the significant imbalance between positive and negative ***, we introduce the AnomalGRN model, focusing on heterogeneity and sparsification to elucidate complex regulatory mechanisms within GRNs. Initially, we consider gene pairs as nodes to construct new networks, thereby converting gene regulation prediction into a node prediction task. Considering the imbalance between positive and negative links in GRNs, we further adapt this issue into a graph anomaly detection (GAD) task, marking the first application of anomaly detection to GRN analysis. Introducing the cosine metric rule enables the AnomalGRN model to differentiate between homogeneity and heterogeneity among nodes in the reconstructed GRNs. The adoption of graph structure sparsification technology reduces noisy data impact and optimizes node representation.

关键词： Gene regulation network (GRN) Link prediction graph anomaly detection Heterogeneity and sparsification

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Improving Generalizability of graph anomaly detection Models via Data Augmentation

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第12期35卷 12721-12735页

作者： Zhou, Shuang Huang, Xiao Liu, Ninghao Zhou, Huachi Chung, Fu-Lai Huang, Long-Kai Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Univ Georgia Athens GA 30602 USA Tencent AI Lab Shenzhen 518057 Guangdong Peoples R China

graph anomaly detection (GAD) has wide applications in real-world networked systems. In many scenarios, people need to identify anomalies on new (sub)graphs, but they may lack labels to train an effective detection model. Since recent semi-supervised GAD methods, which can leverage the available labels as prior knowledge, have achieved superior performance than unsupervised methods, one natural idea is to directly adopt a trained semi-supervised GAD model to the new (sub)graphs for testing. However, we find that existing semi-supervised GAD methods suffer from poor generalization issues, i.e., well-trained models could not perform well on an unseen area (i.e., not accessible in training) of the graph. Motivated by this, we formally define the problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph(s) and the unseen test graph(s). Nevertheless, it is a challenging task since only limited labels are available, and the normal data distribution may differ between training and testing data. Accordingly, we propose a data augmentation method named AugAN (Augmentation for anomaly and Normal distributions) to enrich training data and adopt a customized episodic training strategy for learning with the augmented data. Extensive experiments verify the effectiveness of AugAN in improving model generalizability.

关键词： graph neural networks graph anomaly detection model generalizability data augmentation

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A Comprehensive Survey on graph anomaly detection With Deep Learning

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第12期35卷 12012-12038页

作者： Ma, Xiaoxiao Wu, Jia Xue, Shan Yang, Jian Zhou, Chuan Sheng, Quan Z. Xiong, Hui Akoglu, Leman Macquarie Univ Sch Comp Sydney NSW 2109 Australia Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Rutgers State Univ Dept Management Sci & Informat Syst Piscataway NJ 08854 USA Carnegie Mellon Univ Heinz Coll Informat Syst & Publ Policy Pittsburgh PA 15213 USA

Anomalies are rare observations (e.g., data records or events) that deviate significantly from the others in the sample. Over the past few decades, research on anomaly mining has received increasing interests due to the implications of these occurrences in a wide range of disciplines - for instance, security, finance, and medicine. For this reason, anomaly detection, which aims to identify these rare observations, has become one of the most vital tasks in the world and has shown its power in preventing detrimental events, such as financial fraud, network intrusions, and social spam. The detection task is typically solved by identifying outlying data points in the feature space, which, inherently, overlooks the relational information in real-world data. At the same time, graphs have been prevalently used to represent the structural/relational information, which raises the graph anomaly detection problem - identifying anomalous graph objects (i.e., nodes, edges and sub-graphs) in a single graph, or anomalous graphs in a set/database of graphs. Conventional anomaly detection techniques cannot tackle this problem well because of the complexity of graph data (e.g., irregular structures, relational dependencies, node/edge types/attributes/directions/multiplicities/weights, large scale, etc.). However, thanks to the advent of deep learning in breaking these limitations, graph anomaly detection with deep learning has received a growing attention recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. Specifically, we provide a taxonomy that follows a task-driven strategy and categorizes existing work according to the anomalous graph objects that they can detect. We especially focus on the challenges in this research area and discuss the key intuitions, technical details as well as relative strengths and weaknesses of various techniques in each category. From the survey result

关键词： anomaly detection outlier detection fraud detection rumor detection fake news detection spammer detection misinformation graph anomaly detection deep learning graph embedding graph representation graph neural networks

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Enhancing Multi-view Contrastive Learning for graph anomaly detection 29th

Enhancing Multi-view Contrastive Learning for Graph Anomaly ...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Lu, Qingcheng Wu, Nannan Zhao, Yiming Wang, Wenjun Zu, Quannan Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Tianjin Univ Dept Management & Econ Tianjin Peoples R China

ISBN: (纸本)9789819755714;9789819755721

graph anomaly detection (GAD) has garnered considerable attention owing to its wide-ranging applications in real-world scenarios, including financial network and cybersecurity. Due to the difficulty associated with learning intricate connectivity and high-dimensional attributes in graphs, GAD task poses significant challenges. Existing deep learning methods extract local information between the target node and its neighbors for GAD. Nevertheless, they neglect the global information in the graph which results in suboptimal performance. In this paper, we propose a self-supervised learning framework integrating contrastive learning module and attribute reconstruction module, which can effectively capture both local and global information for GAD. Specifically, we regard original graph as local view, and global view is generated based on this local view. In contrastive learning module, target node and subgraph are sampled from both views to construct multi-scale contrast combinations. With intra- and intre- view contrastive strategy, we can effectively capture both local and global information of anomaly nodes. In attribute reconstruction module, masked graph auto-encoder is employed to further capture anomaly information within the attribute space. Extensive experiments are conducted on six benchmark datasets and results reveal that our approach outperforms the current state-of-the-art methods.

关键词： graph Data Mining graph anomaly detection Contrastive Learning

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Towards Fair graph anomaly detection: Problem, Benchmark Datasets, and Evaluation 24

Towards Fair Graph Anomaly Detection: Problem, Benchmark Dat...

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33rd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Neo, Neng Kai Nigel Lee, Yeon-Chang Jin, Yiqiao Kim, Sang-Wook Kumar, Srijan Georgia Inst Technol Atlanta GA 30332 USA Ulsan Natl Inst Sci & Technol UNIST Ulsan South Korea Hanyang Univ Seoul South Korea

ISBN: (纸本)9798400704369

The Fair graph anomaly detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while avoiding biased predictions against individuals from sensitive subgroups. However, the current literature does not comprehensively discuss this problem, nor does it provide realistic datasets that encompass actual graph structures, anomaly labels, and sensitive attributes. To bridge this gap, we introduce a formal definition of the FairGAD problem and present two novel datasets constructed from the social media platforms Reddit and Twitter. These datasets comprise 1.2 million and 400,000 edges associated with 9,000 and 47,000 nodes, respectively, and leverage political leanings as sensitive attributes and misinformation spreaders as anomaly labels. We demonstrate that our FairGAD datasets significantly differ from the synthetic datasets used by the research community. Using our datasets, we investigate the performance-fairness trade-off in nine existing GAD and non-graph AD methods on five state-of-the-art fairness methods. Code and datasets are available at https://***/nigelnnk/FairGAD.

关键词： graph anomaly detection fairness benchmark datasets

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Unsupervised graph anomaly detection on Directed Attribute Network

Unsupervised Graph Anomaly Detection on Directed Attribute N...

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International Joint Conference on Neural Networks (IJCNN)

作者： Li, Haoyang Wang, Siwei Liu, Xinwang Gan, Xinbiao Natl Univ Def Technol Coll Comp Sci Changsha Peoples R China Acad Mil Sci Intelligent Game & Decis Lab Beijing 100091 Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Attribute networks are commonly used graph structures in complex application domains. With the increasing prominence of security issues, the detection of anomalies in attributed networks has become a widely studied topic. In recent years, anomaly detection methods based on graph deep learning have become increasingly popular. However, current research primarily focuses on undirected attributed networks. These methods fail to fully utilize the directional information of edges when facing directed networks which are widely present in reality, resulting in suboptimal performance. In order to tackle this problem, we propose a new self-supervised framework for anomaly detection in directed attributed networks, referred to as UDGAD. Our framework includes a novel bidirectional subgraph constructing algorithm that distinguishes the direction of edges, effectively leveraging the complex topological structure of directed networks. Furthermore, we propose a self-supervised model based on graph autoencoder and graph neural networks, which explores anomalies from the perspectives of attribute reconstruction and neighborhood matching. Finally, we evaluate anomaly degree of all nodes by multi-round computational evaluation. Experimental results demonstrate that our method outperforms traditional anomaly detection methods on directed attributed networks with ground truth labels.

关键词： graph anomaly detection Directed Attribute Network graph Self-Supervised Learning

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