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Cross-Domain graph Level anomaly detection

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第12期36卷 7839-7850页

作者： Li, Zhong Liang, Sheng Shi, Jiayang van Leeuwen, Matthijs Leiden Univ LIACS NL-2311 EZ Leiden Netherlands Ludwig Maximilians Univ Munchen CIS D-80539 Munich Germany

Existing graph level anomaly detection methods are predominantly unsupervised due to high costs for obtaining labels, yielding sub-optimal detection accuracy when compared to supervised methods. Moreover, they heavily rely on the assumption that the training data exclusively consists of normal graphs. Hence, even the presence of a few anomalous graphs can lead to substantial performance degradation. To alleviate these problems, we propose a cross-domain graph level anomaly detection method, aiming to identify anomalous graphs from a set of unlabeled graphs (target domain) by using easily accessible normal graphs from a different but related domain (source domain). Our method consists of four components: a feature extractor that preserves semantic and topological information of individual graphs while incorporating the distance between different graphs;an adversarial domain classifier to make graph level representations domain-invariant;a one-class classifier to exploit label information in the source domain;and a class aligner to align classes from both domains based on pseudolabels. Experiments on seven benchmark datasets show that the proposed method largely outperforms state-of-the-art methods.

关键词： anomaly detection Feature extraction Semantics Databases Training data Training Transfer learning graph anomaly detection graph neural networks graph transfer learning

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Imbalanced graph-Level anomaly detection via Counterfactual Augmentation and Feature Learning 24

Imbalanced Graph-Level Anomaly Detection via Counterfactual ...

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36th International Conference on Scientific and Statistical Database Management (SSDBM)

作者： Wang, Zitong Luo, Xuexiong Song, Enfeng Bai, Qiuqing Lint, Fu Wuhan Univ Sch Cyber Sci & Engn Wuhan Hubei Peoples R China Macquarie Univ Sch Comp Sydney NSW Australia Wuhan Univ Renmin Hosp Wuhan Hubei Peoples R China Wuhan Univ Sch Comp Sci Sch Cyber Sci & Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9798400710209

graph-level anomaly detection (GLAD) has already gained significant importance and has become a popular field of study, attracting considerable attention across numerous downstream works. The core focus of this domain is to capture and highlight the anomalous information within given graph datasets. In most existing studies, anomalies are often the instances of few. The stark imbalance misleads current GLAD methods to focus on learning the patterns of normal graphs more, further impacting anomaly detection performance. Moreover, existing methods predominantly utilize the inherent features of nodes to identify anomalous graph patterns which is approved suboptimal according to our experiments. In this work, we propose an imbalanced GLAD method via counterfactual augmentation and feature learning. Specifically, we first construct anomalous samples based on counterfactual learning, aiming to expand and balance the datasets. Additionally, we construct a module based on graph Neural Networks (GNNs), which allows us to utilize degree attributes to complement the inherent attribute features of nodes. Then, we design an adaptive weight learning module to integrate features tailored to different datasets effectively to avoid indiscriminately treating all features as equivalent. Furthermore, extensive baseline experiments conducted on public datasets substantiate the robustness and effectiveness. Besides, we apply the model to brain disease datasets, which can prove the generalization capability of our work. The source code of our work is available online.

关键词： graph anomaly detection graph neural networks counterfactual learning

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Motif-Consistent Counterfactuals with Adversarial Refinement for graph-Level anomaly detection 24

Motif-Consistent Counterfactuals with Adversarial Refinement...

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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Xiao, Chunjing Pang, Shikang Tai, Wenxin Huang, Yanlong Trajcevski, Goce Zhou, Fan Henan Univ Kaifeng Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China Iowa State Univ Ames IA USA

ISBN: (纸本)9798400704901

graph-level anomaly detection is significant in diverse domains. To improve detection performance, counterfactual graphs have been exploited to benefit the generalization capacity by learning causal relations. Most existing studies directly introduce perturbations (e.g., flipping edges) to generate counterfactual graphs, which are prone to alter the semantics of generated examples and make them off the data manifold, resulting in sub-optimal performance. To address these issues, we propose a novel approach, Motif-consistent Counterfactuals with Adversarial Refinement (MotifCAR), for graph-level anomaly detection. The model combines the motif of one graph, the core subgraph containing the identification (category) information, and the contextual subgraph (non-motif) of another graph to produce a raw counterfactual graph. However, the produced raw graph might be distorted and cannot satisfy the important counterfactual properties: Realism, Validity, Proximity and Sparsity. Towards that, we present a Generative Adversarial Network (GAN)-based graph optimizer to refine the raw counterfactual graphs. It adopts the discriminator to guide the generator to generate graphs close to realistic data, i.e., meet the property Realism. Further, we design the motif consistency to force the motif of the generated graphs to be consistent with the realistic graphs, meeting the property Validity. Also, we devise the contextual loss and connection loss to control the contextual subgraph and the newly added links to meet the properties Proximity and Sparsity. As a result, the model can generate high-quality counterfactual graphs. Experiments demonstrate the superiority of MotifCAR.

关键词： graph anomaly detection graph neural networks representation learning counterfactual data augmentation

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Towards graph-level anomaly detection via Deep Evolutionary Mapping 23

Towards Graph-level Anomaly Detection via Deep Evolutionary ...

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29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Ma, Xiaoxiao Wu, Jia Yang, Jian Sheng, Quan Z. Macquarie Univ Sch Comp Sydney NSW Australia

ISBN: (纸本)9798400701030

graph-level anomaly detection aims at capturing anomalous individual graphs in a graph set. Due to its significance in various real-world application fields, e.g., identifying rare molecules in chemistry and detecting potential frauds in online social networks, graph-level anomaly detection has received great attention recently. In distinction from node- and edge-level anomaly detection that is devoted to identifying anomalies on a single graph, graph-level anomaly detection faces more significant challenges because both the intra- and inter-graph structural and attribute patterns need to be taken into account to distinguish anomalies that exhibit deviating structures, rare attributes or the both. Although deep graph representation learning shows effectiveness in fusing high-level representations and capturing characters of individual graphs, most of the existing works are defective in graph-level anomaly detection because of their limited capability in exploring information across graphs, the imbalanced data distribution of anomalies, and low interpretability of the black-box graph neural networks (GNNs). To overcome these limitations, we propose a novel deep evolutionary graph mapping framework named GmapAD1, which can adaptively map each graph into a new feature space based on its similarity to a set of representative nodes chosen from the graph set. By automatically adjusting the candidate nodes using a specially designed evolutionary algorithm, anomalies and normal graphs are mapped to separate areas in the new feature space where a clear boundary between them can be learned. The selected candidate nodes can therefore be regarded as a benchmark for explaining anomalies because anomalies are more dissimilar/similar to the benchmark than normal graphs. Through our extensive experiments on nine real-world datasets, we demonstrate that exploring both intra- and inter-graph structural and attribute information is critical to spot anomalous graphs, and our method has

关键词： graph anomaly detection anomaly detection graph representation learning differential evolution

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Higher-order Enhanced Contrastive-based graph anomaly detection Without graph Augmentation

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PATTERN RECOGNITION 2025年 167卷

作者： Hu, Jingtao Wang, Siwei Duan, Jingcan Jin, Hu Liu, Xinwang Zhu, En Acad Mil Sci Beijing 100091 Peoples R China Unit 31592 PLA Beijing 100041 Peoples R China Natl Univ Def Technol Changsha 410073 Hunan Peoples R China

graph anomaly detection (GAD) has been widely applied in various attributed graph data mining domains, such as financial fraud and academic citation networks. Its goal is to detect instances in graph data significantly different from other data. Existing GAD methods often modify node features or structures based on graph augmentation to improve the robustness of the model, which may lead to poor model performance and efficiency due to anomaly confusion. To tackle the aforementioned issues, we propose a novel Higher-order Enhanced Contrastive-based graph anomaly detection without graph Augmentation (HEC-GAD) in this paper. To avoid anomaly distortion, we no longer perform graph augmentation, but instead sample from ego-net and expand it to high-order neighbors to generate multi-view subgraph of the target node. To more efficiently mine anomalies in topology and attribute information, we conduct intra-view node-subgraph and inter-view subgraph-subgraph contrastive learning, and identify abnormal nodes through the reconstruction error of nodes. Comprehensive experimental results on eight artificial and real-world benchmark datasets demonstrate the effectiveness of our proposed higher-order enhanced multi-view contrastive graph anomaly detection method.

关键词： graph anomaly detection graph contrastive learning Multi-view contrastive learning

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graph anomaly detection Based on Steiner Connectivity and Density

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PROCEEDINGS OF THE IEEE 2018年第5期106卷 829-845页

作者： Cadena, Jose Chen, Feng Vullikanti, Anil Virginia Tech Dept Comp Sci Blacksburg VA 24060 USA Virginia Tech Biocomplex Inst Blacksburg VA 24060 USA Lawrence Livermore Natl Lab Machine Learning Grp Livermore CA 94550 USA SUNY Albany Dept Comp Sci Albany NY 12222 USA

Detecting "hotspots" and "anomalies" is a recurring problem with a wide range of applications, such as social network analysis, epidemiology, finance, and biosurveillance, among others. Networks are a common abstraction in these applications for representing complex relationships. Typically, these networks are dynamic-, i.e., they evolve over time. A number of methods have been proposed for anomaly detection in such dynamic network data sets, which are primarily based on changes in network properties. We provide a survey of the various formulations of anomaly detection in dynamic networks with a focus on "window-based" methods. Window-based methods first define a time window of past network snapshots to model normal behavior and then mark a snapshot as anomalous if it has significantly different patterns from those observed in the time window. We describe two classes of techniques: 1) generalizations of Steiner connectivity;and 2) dense subgraph mining. Both have been used extensively in window-based graph anomaly detection. We summarize the key problem formulations that have been studied using these approaches, and we describe details of some of the main techniques.

关键词： Approximation algorithms dense subgraph mining graph anomaly detection graph mining parameterized complexity scan statistics

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A simple graph embedding for anomaly detection in a stream of heterogeneous labeled graphs

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PATTERN RECOGNITION 2021年 112卷 107746-107746页

作者： Kiouche, Abd Errahmane Lagraa, Sofiane Amrouche, Karima Seba, Hamida Univ Lyon 1 Univ Lyon CNRS LIRISUMR5205 F-69622 Lyon France Ecole Natl Super Informat Http Www Esi Dz Lab Commun Syst Informat LCSI BP 68M Oued Smar 16309 Alger Algeria Univ Luxembourg SnT Interdisciplinary Ctr Secur Reliabil & Trust Luxembourg Luxembourg

In this work, we propose a new approach to detect anomalous graphs in a stream of directed and labeled heterogeneous edges. The stream consists of a sequence of edges derived from different graphs. Each of these dynamic graphs represents the evolution of a specific activity in a monitored system whose events are acquired in real-time. Our approach is based on graph clustering and uses a simple graph embedding based on substructures and graph edit distance. Our graph representation is flexible and updates incrementally the graph vectors as soon as a new edge arrives. This allows the detection of anomalies in real-time which is an important requirement for sensitive applications such as cyber-security. Our implementation results prove the effectiveness of our approach in terms of accuracy of detection and time processing. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： graph anomaly detection graph stream graph embedding graph edit distance

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When bipartite graph learning meets anomaly detection in attributed networks: Understand abnormalities from each attribute

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NEURAL NETWORKS 2025年 185卷 107194页

作者： Peng, Zhen Wang, Yunfan Lin, Qika Dong, Bo Shen, Chao Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Natl Univ Singapore Saw Swee Hock Sch Publ Hlth Singapore Singapore Xi An Jiao Tong Univ Sch Distance Educ Xian Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian Peoples R China Univ Virginia Dept Comp Sci Charlottesville VA USA

Detecting anomalies in attributed networks has become a subject of interest in both academia and industry due to its wide spectrum of applications. Although most existing methods achieve desirable performance by the merit of various graph neural networks, the way they bundle node-affiliated multidimensional attributes into a whole for embedding calculation hinders their ability to model and analyze anomalies at the fine-grained feature level. To characterize anomalies from each feature dimension, we propose EAGLE, a deep framework based on bipartitE graph learninG for anomaly detection. Specifically, we disentangle instances and attributes as two disjoint and independent node sets, then formulate the input attributed network as an intra-connected bipartite graph that involves two different relations: edges across two types of nodes described by attribute values, and links between nodes of the same type recorded in the network topology. By learning a self- supervised edge-level prediction task, named affinity inference, EAGLE has good physical sense in explaining abnormal deviations from each attribute. Experiments corroborate the effectiveness of EAGLE under transductive and inductive task settings. Moreover, case studies illustrate that EAGLE is more user-friendly as it opens the door for humans to understand abnormalities from the perspective of different feature combinations.

关键词： graph anomaly detection Bipartite graph modeling Self-supervised learning

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anomaly detection Over Multi-Relational graphs Using graph Structure Learning and Multi-Scale Meta-Path graph Aggregation

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IEEE ACCESS 2025年 13卷 60303-60316页

作者： Zhang, Chi Jeong, Junho Jung, Jin-Woo Dongguk Univ Dept Comp Sci & Engn Seoul 04620 South Korea

graph Neural Networks (GNNs) have recently achieved remarkable success in various learning tasks involving graph-structured data. However, their application to multi-relational graph anomaly detection problems on real-world datasets presents several challenges that significantly hinder performance: the structural noise and inconsistencies inherent in real-world graph data, difficulties in aggregating information across multiple relation types, and the imbalanced class labels. To address these limitations, we introduce a graph structure learning layer designed to refine the original, noisy graph structure, enhancing the representation of node relationships. This enables our model to effectively handle inconsistencies and structural noise present in the original graph data with the augmented graph topology. To further exploit the semantic information embedded within the multi-relational graph data, we developed a multi-scale aggregation layer that facilitates information propagation and fusion through multi-relational levels, utilizing automatically and implicitly extracted meta-path graphs. Additionally, to handle the impact of imbalanced class labels, which often degrades the classification performance of GNN-based approaches, we introduce a score reweighting mechanism that balances probability scores between minority and majority classes. This strategy improves the model's learning ability, leading to improved class distinction. Comprehensive experiments are conducted using two popular real-world review datasets to validate the effectiveness of our proposed model. Empirical results demonstrate that our approach consistently outperforms state-of-the-art baselines across three evaluation metrics, namely Average Precision, AUC and GMean.

关键词： anomaly detection Noise Data models Training Reviews Image edge detection Weight measurement Noise measurement Message passing graph anomaly detection graph neural networks graph structure learning meta-path graphs meta-path graphs

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Comparing Hyperbolic graph Embedding models on anomaly detection for Cybersecurity 24

Comparing Hyperbolic Graph Embedding models on Anomaly Detec...

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19th International Conference on Availability, Reliability, and Security (ARES)

作者： Miliani, Mohamed Yacine Touahria Sadat, Souhail Abdelmouaiz Haddad, Mohammed Ecole Natl Super Informat Algeries Algeria Univ Lyon UCBL INSA Lyon CNRSLIRISUMR5205 Villeurbanne France

ISBN: (纸本)9798400717185

graph-based anomaly detection has emerged as a powerful tool in cybersecurity for identifying malicious activities within computer systems and networks. While existing approaches often rely on embedding graphs in Euclidean space, recent studies have suggested that hyperbolic space provides a more suitable geometry for capturing the inherent hierarchical and complex relationships present in graph data. In this paper, we explore the efficacy of hyperbolic graph embedding for anomaly detection in the context of cybersecurity. We conduct a comparison of six state-of-the-art hyperbolic graph embedding methods, evaluating their performance on a well-known intrusion detection dataset. Our analysis reveals the strengths and limitations of each method, demonstrating the potential of hyperbolic graph embedding for enhancing security.

关键词： graph representation learning graph anomaly detection hyperbolic space non-Euclidean embeddings

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