{"total":40,"rows":[{"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":"2409.09957","title":"Deep Graph Anomaly Detection: A Survey and New Perspectives","created":"2024-09-16","cited_by_count":40,"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":"2502.13308","title":"A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection","created":"2025-02-18","cited_by_count":24,"area":null},{"id":"2402.11887","title":"Generative Semi-supervised Graph Anomaly Detection","created":"2024-02-19","cited_by_count":22,"area":null},{"id":"2307.00755","title":"Graph-level Anomaly Detection via Hierarchical Memory Networks","created":"2023-07-03","cited_by_count":16,"area":null},{"id":"2311.06835","title":"Open-Set Graph Anomaly Detection via Normal Structure Regularisation","created":"2023-11-12","cited_by_count":11,"area":null},{"id":"2405.17525","title":"SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection","created":"2024-05-27","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":"2409.09770","title":"Cluster Aware Graph Anomaly Detection","created":"2024-09-15","cited_by_count":8,"area":null},{"id":"2602.18793","title":"From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection","created":"2026-02-21","cited_by_count":8,"area":null},{"id":"2606.18621","title":"Towards Anomaly Detection on Relational Data","created":"2026-06-17","cited_by_count":5,"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":"2512.18133","title":"Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection","created":"2025-12-19","cited_by_count":4,"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":"2605.20032","title":"CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection","created":"2026-05-19","cited_by_count":3,"area":null},{"id":"2307.14387","title":"Coupled-Space Attacks against Random-Walk-based Anomaly Detection","created":"2023-07-26","cited_by_count":2,"area":null},{"id":"2308.14340","title":"HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks","created":"2023-08-28","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":"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":"2409.11618","title":"PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities","created":"2024-09-18","cited_by_count":1,"area":null},{"id":"2411.17296","title":"GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers","created":"2024-11-26","cited_by_count":1,"area":null},{"id":"2601.21171","title":"AC2L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detection","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2605.26857","title":"Generalist Graph Anomaly Detection via Prototype-Based Distillation","created":"2026-05-26","cited_by_count":1,"area":null},{"id":"2407.05934","title":"Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels","created":"2024-07-08","cited_by_count":0,"area":null},{"id":"2504.14250","title":"Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection","created":"2025-04-19","cited_by_count":0,"area":null},{"id":"2508.12278","title":"CRoC: Context Refactoring Contrast for Graph Anomaly Detection with Limited Supervision","created":"2025-08-17","cited_by_count":0,"area":null},{"id":"2510.02014","title":"Normality Calibration in Semi-supervised Graph Anomaly Detection","created":"2025-10-02","cited_by_count":0,"area":null},{"id":"2510.11827","title":"Combining Euclidean and Hyperbolic Representations for Node-level Anomaly Detection","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2602.01454","title":"Modeling Topological Impact on Node Attribute Distributions in Attributed Graphs","created":"2026-02-01","cited_by_count":0,"area":null},{"id":"2603.09349","title":"TA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detection","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2605.10242","title":"When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection","created":"2026-05-11","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":"2605.20879","title":"NeighborDiv: Training-free Zero-shot Generalist Graph Anomaly Detection via Neighbor Diversity","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2605.26446","title":"DDGAD: Disagreement-Driven Graph Anomaly Detection via Adapt-Then-Combine","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2606.12673","title":"A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction","created":"2026-06-10","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":"2609.33370","title":"The Selection Rule Decides the Winner: A Pre-Registered Audit of Open-Set Graph Anomaly Detection","created":"2026-09-27","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}