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33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Jin, Wangyu Ma, Huifang Zhang, Yingyue Li, Zhixin Chang, Liang Northwest Normal Univ Coll Comp Sci & Engn Lanzhou Gansu Peoples R China Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin Guangxi Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin Guangxi Peoples R China

ISBN: (纸本)9783031723438;9783031723445

Attributed graph anomaly detection aims to identify abnormal nodes that significantly differ from most nodes in terms of their attribute or structure. Recent graph contrastive learning methods, which follow an augmenting-contrasting learning scheme, have shown great potential. However, these methods still have two key limitations. Firstly, the contrastive views generated through random perturbation may not conform to abnormal patterns. Secondly, the inability to effectively discriminate heterophilic edges in anomalous networks can adversely impact the detection performance. To this end, we propose a Heterophily-aware Contrastive Learning network (HCL for abbreviation) for attributed graph anomaly detection. Our approach addresses the aforementioned limitations by integrating human knowledge of anomaly patterns for constructing contrastive views and identifying edge homophily/heterophily using an unsupervised edge discriminator. Additionally, a dual-channel encoder is designed to capture representative node representations from discriminated edges. Extensive experiments on four public benchmark datasets demonstrate that our method is competitive with the most advanced baseline.

关键词： graph anomaly detection Contrastive Learning graph Neural Networks Self-supervised Learning Heterophily

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TransGAD: A Transformer-Based Autoencoder for graph anomaly detection 29th

TransGAD: A Transformer-Based Autoencoder for Graph Anomaly ...

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

作者： Guo, Zehao Wu, Nannan Zhao, Yiming Wang, Wenjun Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

ISBN: (纸本)9789819755714;9789819755721

graph anomaly detection, aimed at identifying anomalous patterns that significantly differ from other nodes, has drawn widespread attention in recent years. Due to the complex topological structures and attribute information inherent in graphs, conventional methods often struggle to effectively identify anomalies. Deep anomaly detection methods based on graph Neural Networks (GNNs) have achieved significant success. However, they face the challenge of not only obtaining limited neighborhood information but over-smoothing. Over-smoothing is the phenomenon where the representations of nodes gradually become similar and flattened across multiple convolutional layers, thereby limiting the comprehensive learning of neighborhood information. Therefore, we propose a novel anomaly detection framework, TransGAD, to address these challenges. Inspired by the graph Transformer, we introduce a Transformer-based autoencoder. Treating each node as a sequence and its neighborhood as tokens in the sequence, this autoencoder captures both local and global information. We incorporate cosine positional encoding and masking strategy to obtain more informative node representations and leverage reconstruction error for improved anomaly detection. Experimental results on seven datasets demonstrate that our approach outperforms the state-of-the-art methods.

关键词： graph anomaly detection Transformer Autoencoder

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A Synergistic Approach for graph anomaly detection With Pattern Mining and Feature Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第6期33卷 2393-2405页

作者： Zhao, Tong Jiang, Tianwen Shah, Neil Jiang, Meng Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA Harbin Inst Technol Res Ctr Social Comp & Informat Retrieval Harbin 150001 Peoples R China Snap Inc Snap Res Seattle WA 98121 USA

Detecting anomalies on graph data has two types of methods. One is pattern mining that discovers strange structures globally such as quasi-cliques, bipartite cores, or dense blocks in the graph's adjacency matrix. The other is feature learning that mainly uses graph neural networks (GNNs) to aggregate information from local neighborhood into node representations. However, there is a lack of study that utilizes both the global and local information for graph anomaly detection. In this article, we propose a synergistic approach that leverages pattern mining to inform the GNN algorithms on how to aggregate local information through connections to capture the global patterns. Specifically, it uses a GNN encoder to perform feature aggregation, and the pattern mining algorithms supervise the GNN training process through a novel loss function. We provide theoretical analysis on the effectiveness of the loss function, as well as empirical analysis on the proposed approach across a variety of GNN algorithms and pattern mining methods. Experiments on real-world data show that the synergistic approach performs significantly better than existing graph anomaly detection methods.

关键词： anomaly detection Bipartite graph Task analysis Feature extraction Aggregates Prediction algorithms Collaboration graph anomaly detection graph neural network (GNN) graph pattern mining unsupervised learning

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Normality Learning-based graph anomaly detection via Multi-Scale Contrastive Learning 23

Normality Learning-based Graph Anomaly Detection via Multi-S...

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31st ACM International Conference on Multimedia (MM)

作者： Duan, Jingcan Zhang, Pei Wang, Siwei Hu, Jingtao Jin, Hu Zhang, Jiaxin Zhou, Haifang Liu, Xinwang Natl Univ Def Technol Changsha Hunan Peoples R China Intelligent Game & Decis Lab Beijing Peoples R China

ISBN: (纸本)9798400701085

graph anomaly detection (GAD) has attracted increasing attention in machine learning and data mining. Recent works have mainly focused on how to capture richer information to improve the quality of node embeddings for GAD. Despite their significant advances in detection performance, there is still a relative dearth of research on the properties of the task. GAD aims to discern the anomalies that deviate from most nodes. However, the model is prone to learn the pattern of normal samples which make up the majority of samples. Meanwhile, anomalies can be easily detected when their behaviors differ from normality. Therefore, the performance can be further improved by enhancing the ability to learn the normal pattern. To this end, we propose a normality learning-based GAD framework via multi-scale contrastive learning networks (NLGAD for abbreviation). Specifically, we first initialize the model with the contrastive networks on different scales. To provide sufficient and reliable normal nodes for normality learning, we design an effective hybrid strategy for normality selection. Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimate of normality so that anomalous nodes can be more easily distinguished. Eventually, extensive experiments on six benchmark graph datasets demonstrate the effectiveness of our normality learning-based scheme on GAD. Notably, the proposed algorithm improves the detection performance (up to 5.89% AUC gain) compared with the state-of-the-art methods. The source code is released at https://***/FelixDJC/NLGAD.

关键词： graph anomaly detection Normality Learning Multi-Scale Contrastive Learning

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GADAL: An Active Learning Framework for graph anomaly detection 6th

GADAL: An Active Learning Framework for Graph Anomaly Detect...

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6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM)

作者： Chang, Wenjing Yu, Jianjun Zhou, Xiaojun Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9783031251573;9783031251580

graph Neural Networks (GNNs) have been widely used in graph-based anomaly detection tasks, and these methods require a sufficient amount of labeled data to achieve satisfactory performance. However, the high cost for data annotation leads to some well-designed algorithms in low practicality in real-world tasks. Active learning has been used to find a trade-off between labeling cost and model performance, while few prior works take it into anomaly detection. Therefore, we propose GADAL, a novel Active Learning framework for graph anomaly detection, which employs a multi-aspects query strategy to achieve high performance within a limited budget. First, we design an abnormal-aware query strategy based on the scalable sliding window to enrich abnormal patterns and alleviate the class imbalance problem. Second, we design an inconsistency-aware query strategy based on the effective degree to capture the most specificity nodes in information aggregation. Then we provide a hybrid solution for the above query strategies. Empirical studies demonstrate that our query strategy significantly outperforms other strategies, and GADAL achieves a comparable performance to the stateof-art anomaly detection methods within less than 3% of the budget.

关键词： graph anomaly detection Active learning graph neural networks

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Reinforcement Neighborhood Selection for Unsupervised graph anomaly detection 23

Reinforcement Neighborhood Selection for Unsupervised Graph ...

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23rd IEEE International Conference on Data Mining (IEEE ICDM)

作者： Beil, Yuanchen Zhoult, Sheng Tan, Qiaoyu Xu, Hao Chen, Hao Li, Zhao But, Jiajun Zhejiang Univ Zhejiang Prov Key Lab Serv Robot Hangzhou Peoples R China New York Univ Shanghai Shanghai Peoples R China Unaffiliated Beijing Peoples R China Hong Kong Polytechn Univ Hong Kong Peoples R China

ISBN: (纸本)9798350307887

Unsupervised graph anomaly detection is crucial for various practical applications as it aims to identify anomalies in a graph that exhibit rare patterns deviating significantly from the majority of nodes. Recent advancements have utilized graph Neural Networks (GNNs) to learn high-quality node representations for anomaly detection by aggregating information from neighborhoods. However, the presence of anomalies may render the observed neighborhood unreliable and result in misleading information aggregation for node representation learning Selecting the proper neighborhood is critical for graph anomaly detection but also challenging due to the absence of anomalyoriented guidance and the interdependence with representation learning. To address these issues, we utilize the advantages of reinforcement learning in adaptively learning in complex enviromnents and propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph anomaly detection (RAND). RAND begins by enriching the candidate neighbor pool of the given central node with multiple types of indirect neighbors. Next, RANI) designs a tailored reinforcement anomaly evaluation module to assess the reliability and reward of considering the given neighbor. Filially, RANI) selects the most reliable subset of neighbors based on these rewards and introduces an anomaly -aware aggregator to amplify messages from reliable neighbors while diminishing messages from unreliable ones. Extensive experiments on both three synthetic and two real-world datasets demonstrate that RAND outperforms the stale-of-the-art methods.

关键词： graph anomaly detection unsupervised learning neighborhood selection message passing

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Rethinking graph anomaly detection: A self-supervised Group Discrimination paradigm with Structure-Aware

Rethinking graph anomaly detection: A self-supervised Group ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yan, Junyi Zuo, Enguang Chen, Chen Chen, Cheng Zhong, Jie Li, Tianle Lv, Xiaoyi Xinjiang Univ Coll Software Urumqi Xinjiang Peoples R China Xinjiang Univ Coll Informat Sci & Engn Urumqi Xinjiang Peoples R China

ISBN: (纸本)9781665468916

Structural anomalies are the core problem in graph anomaly detection. However, the current mainstream self-supervised graph anomaly detection models do not directly model structural anomalies and their expensive time consumption limits the efficiency of graph anomaly detection. For this reason, we rethink graph anomaly detection and propose a self-supervised Group Discrimination paradigm with Structure-Aware (GDSA). Our model can be explicitly aware of the graph topology changes by multi-view structure disturbance. Moreover, GDSA transforms graph anomaly detection into discriminating the scalar summaries of positive and negative group nodes. The results of extensive experiments on four benchmark datasets show that GDSA outperforms current state-of-the-art methods, with the most significant AUC performance improvement of 28.7%. Notably, in scalability testing on a large-scale dataset, the training time and testing time of GDSA are 1181.0x and 5064.7x faster than the baseline, respectively, with 61.9% savings in memory usage.

关键词： graph anomaly detection Self-supervised learning Group discrimination Structure-aware

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Few-shot Message-Enhanced Contrastive Learning for graph anomaly detection 29

Few-shot Message-Enhanced Contrastive Learning for Graph Ano...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Fan Wang, Nan Wen, Xuezhi Gao, Meiqi Guo, Chaoqun Zhao, Xibin University of Science and Technology of China School of Computer Science and Technology Anhui China Beijing Jiaotong University School of Software Engineering Beijing China Tsinghua University School of Software Engineering Beijing China

ISBN: (纸本)9798350330717

graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in various domains of information security, including network intrusion, financial fraud, and malicious comments, et al. Existing methods are primarily developed in an unsupervised manner due to the challenge in obtaining labeled data. For lack of guidance from prior knowledge in unsupervised manner, the identified anomalies may prove to be data noise or individual data instances. In real-world scenarios, a limited batch of labeled anomalies can be captured, making it crucial to investigate the few-shot problem in graph anomaly detection. Taking advantage of this potential, we propose a novel few-shot graph anomaly detection model called FMGAD (Few-shot Message-Enhanced Contrastive-based graph anomaly Detector). FMGAD leverages a self-supervised contrastive learning strategy within and across views to capture intrinsic and transferable structural representations. Furthermore, we propose the Deep-GNN message-enhanced reconstruction module, which extensively exploits the few-shot label information and enables long-range propagation to disseminate supervision signals to deeper unlabeled nodes. This module in turn assists in the training of self-supervised contrastive learning. Comprehensive experimental results on six real-world datasets demonstrate that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies. © 2023 IEEE.

关键词： Deep-GNN few-shot graph anomaly detection

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Improving graph Convolutional Network with Learnable Edge Weights and Edge-Node Co-Embedding for graph anomaly detection

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SENSORS 2024年第8期24卷 2591页

作者： Tan, Xiao Yang, Jianfeng Zhao, Zhengang Xiao, Jinsheng Li, Chengwang Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Univ Sci & Technol China Sch Software Engn Suzhou 215123 Jiangsu Peoples R China China Jiliang Univ Coll Sci Hangzhou 310018 Peoples R China

The era of Industry 4.0 is gradually transforming our society into a data-driven one, which can help us uncover valuable information from accumulated data, thereby improving the level of social governance. The detection of anomalies, is crucial for maintaining societal trust and fairness, yet it poses significant challenges due to the ubiquity of anomalies and the difficulty in identifying them accurately. This paper aims to enhance the performance of the current graph Convolutional Network (GCN)-based graph anomaly detection (GAD) algorithm on datasets with extremely low proportions of anomalous labels. This goal is achieved through modifying the GCN network structure and conducting feature extraction, thus fully utilizing three types of information in the graph: node label information, node feature information, and edge information. Firstly, we theoretically demonstrate the relationship between label propagation and feature convolution, indicating that the Label Propagation Algorithm (LPA) can serve as a regularization penalty term for GCN, aiding in training and enabling learnable edge weights, providing a basis for incorporating node label information into GCN networks. Secondly, we introduce a method to aggregate node and edge features, thereby incorporating edge information into GCN networks. Finally, we design different GCN trainable weights for node features and co-embedding features. This design allows different features to be projected into different spaces, greatly enhancing model expressiveness. Experimental results on the Dgraph dataset demonstrate superior AUC performance compared to baseline models, highlighting the feasibility and efficacy of the proposed approach in addressing GAD tasks in the scene with extremely low proportions of anomalous data.

关键词： graph anomaly detection label propagation graph convolutional neural networks semi-supervised learning

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Multi-view discriminative edge heterophily contrastive learning network for attributed graph anomaly detection

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期255卷

作者： Jin, Wangyu Ma, Huifang Zhang, Yingyue Li, Zhixin Chang, Liang Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Gansu Peoples R China Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin 541004 Guangxi Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Guangxi Peoples R China

Attributed graph anomaly detection aims to identify abnormal nodes that significantly differ from most nodes in terms of their attribute or structure. Recent graph contrastive learning methods, which follow an augmenting -contrasting learning scheme, have shown promising results. However, these methods still have two key limitations. Firstly, the contrastive views generated through random perturbation may not conform to abnormal patterns. Secondly, the inability to effectively discriminate heterophilic edges in anomalous networks can adversely impact the detection performance. To this end, we propose a M ulti -view discriminative E dge heterophi L y c O ntrastive learning N etwork (MELON) for attributed graph anomaly detection. Our approach addresses the aforementioned limitations by integrating human knowledge of different anomaly patterns for data augmentation and designing an edge discriminator to identify edge homophily/heterophily in an unsupervised manner. In addition, a dual -channel encoder is also designed to capture representative representations of the nodes from the discriminated homophilic/heterophilic edges. We then use a decoder to reconstruct the original network from the node representations learned by the dual -channel encoder and rank nodes according to their reconstruction error as the anomaly metric. Extensive experiments on four public benchmark datasets demonstrate that our proposed method outperforms state-of-the-art baselines.

关键词： graph anomaly detection Contrastive learning graph neural networks Self-supervised learning Heterophily

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