{"total":65,"rows":[{"id":"2307.03759","title":"A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection","created":"2023-07-07","cited_by_count":145,"area":null},{"id":"2212.00535","title":"Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View","created":"2022-12-01","cited_by_count":55,"area":null},{"id":"2405.16771","title":"ARC: A Generalist Graph Anomaly Detector with In-Context Learning","created":"2024-05-27","cited_by_count":48,"area":null},{"id":"2306.00006","title":"Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection","created":"2023-05-29","cited_by_count":43,"area":null},{"id":"2409.09957","title":"Deep Graph Anomaly Detection: A Survey and New Perspectives","created":"2024-09-16","cited_by_count":40,"area":null},{"id":"2205.04816","title":"Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks","created":"2022-05-10","cited_by_count":28,"area":null},{"id":"2208.07448","title":"Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis","created":"2022-08-15","cited_by_count":28,"area":null},{"id":"2410.14886","title":"Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts","created":"2024-10-18","cited_by_count":28,"area":null},{"id":"2310.11676","title":"PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection","created":"2023-10-18","cited_by_count":25,"area":null},{"id":"2205.05173","title":"Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook","created":"2022-05-10","cited_by_count":24,"area":null},{"id":"2209.14930","title":"Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges","created":"2022-09-29","cited_by_count":23,"area":null},{"id":"2402.11887","title":"Generative Semi-supervised Graph Anomaly Detection","created":"2024-02-19","cited_by_count":22,"area":null},{"id":"2206.04149","title":"A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems","created":"2022-06-08","cited_by_count":20,"area":null},{"id":"2307.08390","title":"Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection","created":"2023-07-17","cited_by_count":18,"area":null},{"id":"2210.12941","title":"Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method","created":"2022-10-24","cited_by_count":15,"area":null},{"id":"2202.05525","title":"From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach","created":"2022-02-11","cited_by_count":13,"area":null},{"id":"2206.04864","title":"Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction","created":"2022-06-10","cited_by_count":11,"area":null},{"id":"2306.10534","title":"Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation","created":"2023-06-18","cited_by_count":11,"area":null},{"id":"2405.11034","title":"Safety in Graph Machine Learning: Threats and Safeguards","created":"2024-05-17","cited_by_count":11,"area":null},{"id":"2508.10594","title":"FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection","created":"2025-08-14","cited_by_count":10,"area":null},{"id":"2211.15255","title":"ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness","created":"2022-11-28","cited_by_count":8,"area":null},{"id":"2409.09770","title":"Cluster Aware Graph Anomaly Detection","created":"2024-09-15","cited_by_count":8,"area":null},{"id":"2109.04021","title":"Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal Videos","created":"2021-09-09","cited_by_count":7,"area":null},{"id":"2312.05526","title":"Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection","created":"2023-12-09","cited_by_count":7,"area":null},{"id":"2211.08378","title":"Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction","created":"2022-11-15","cited_by_count":6,"area":null},{"id":"2307.15244","title":"BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection","created":"2023-07-28","cited_by_count":6,"area":null},{"id":"2308.01947","title":"Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model","created":"2023-08-03","cited_by_count":6,"area":null},{"id":"2309.06034","title":"Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning","created":"2023-09-12","cited_by_count":6,"area":null},{"id":"2310.06261","title":"Self-Discriminative Modeling for Anomalous Graph Detection","created":"2023-10-10","cited_by_count":6,"area":null},{"id":"2301.06794","title":"Subgraph Centralization: A Necessary Step for Graph Anomaly Detection","created":"2023-01-17","cited_by_count":5,"area":null},{"id":"2410.08629","title":"Towards Cross-domain Few-shot Graph Anomaly Detection","created":"2024-10-11","cited_by_count":5,"area":null},{"id":"2508.00513","title":"Text-Attributed Graph Anomaly Detection via Multi-Scale Cross- and Uni-Modal Contrastive Learning","created":"2025-08-01","cited_by_count":5,"area":null},{"id":"2305.02496","title":"Revisiting Graph Contrastive Learning for Anomaly Detection","created":"2023-05-04","cited_by_count":4,"area":null},{"id":"2506.15448","title":"Semi-supervised Graph Anomaly Detection via Robust Homophily Learning","created":"2025-06-18","cited_by_count":4,"area":null},{"id":"2507.14387","title":"Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructures","created":"2025-07-18","cited_by_count":4,"area":null},{"id":"2304.01226","title":"Abnormal Event Detection via Hypergraph Contrastive Learning","created":"2023-04-02","cited_by_count":3,"area":null},{"id":"2311.06153","title":"GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps","created":"2023-11-10","cited_by_count":3,"area":null},{"id":"2508.00507","title":"Court of LLMs: Evidence-Augmented Generation via Multi-LLM Collaboration for Text-Attributed Graph Anomaly Detection","created":"2025-08-01","cited_by_count":3,"area":null},{"id":"2603.01806","title":"GCTAM: Global and Contextual Truncated Affinity Combined Maximization Model For Unsupervised Graph Anomaly Detection","created":"2026-03-02","cited_by_count":3,"area":null},{"id":"2307.04097","title":"Restricted Generative Projection for One-Class Classification and Anomaly Detection","created":"2023-07-09","cited_by_count":2,"area":null},{"id":"2411.12556","title":"UMGAD: Unsupervised Multiplex Graph Anomaly Detection","created":"2024-11-19","cited_by_count":2,"area":null},{"id":"2501.15035","title":"Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs","created":"2025-01-25","cited_by_count":2,"area":null},{"id":"2502.14293","title":"Cross-Domain Graph Anomaly Detection via Test-Time Training with Homophily-Guided Self-Supervision","created":"2025-02-20","cited_by_count":2,"area":null},{"id":"2601.19102","title":"OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection","created":"2026-01-27","cited_by_count":2,"area":null},{"id":"2304.05176","title":"Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph","created":"2023-04-11","cited_by_count":1,"area":null},{"id":"2305.10668","title":"MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection","created":"2023-05-18","cited_by_count":1,"area":null},{"id":"2401.01625","title":"SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks","created":"2024-01-03","cited_by_count":1,"area":null},{"id":"2406.00452","title":"Towards a Unified Framework of Clustering-based Anomaly Detection","created":"2024-06-01","cited_by_count":1,"area":null},{"id":"2407.12440","title":"GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs","created":"2024-07-17","cited_by_count":1,"area":null},{"id":"2502.16076","title":"Harnessing Feature Resonance under Arbitrary Target Alignment for Out-of-Distribution Node Detection","created":"2025-02-22","cited_by_count":1,"area":null},{"id":"2505.18002","title":"Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective","created":"2025-05-23","cited_by_count":1,"area":null},{"id":"2209.10168","title":"Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation","created":"2022-09-21","cited_by_count":0,"area":null},{"id":"2304.14114","title":"Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information","created":"2023-04-27","cited_by_count":0,"area":null},{"id":"2403.09039","title":"Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality","created":"2024-03-14","cited_by_count":0,"area":null},{"id":"2404.16366","title":"Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection","created":"2024-04-25","cited_by_count":0,"area":null},{"id":"2405.14742","title":"HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning","created":"2024-05-23","cited_by_count":0,"area":null},{"id":"2408.01872","title":"Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples","created":"2024-08-03","cited_by_count":0,"area":null},{"id":"2410.15355","title":"LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Space","created":"2024-10-20","cited_by_count":0,"area":null},{"id":"2501.14694","title":"Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection","created":"2025-01-24","cited_by_count":0,"area":null},{"id":"2506.23469","title":"Reconciling Attribute and Structural Anomalies for Improved Graph Anomaly Detection","created":"2025-06-30","cited_by_count":0,"area":null},{"id":"2509.22544","title":"HyCoVAD: A Hybrid SSL-LLM Model for Complex Video Anomaly Detection","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2510.12401","title":"Enhanced Pre-training of Graph Neural Networks for Million-Scale Heterogeneous Graphs","created":"2025-10-14","cited_by_count":0,"area":null},{"id":"2605.11749","title":"Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2608.16018","title":"RagGAD: Rationale-Aware Conditional Gaussian Mixture Normalizing Flow for Unsupervised Graph Anomaly Detection","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2609.38424","title":"Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}