{"total":20,"rows":[{"id":"2409.09957","title":"Deep Graph Anomaly Detection: A Survey and New Perspectives","created":"2024-09-16","cited_by_count":40,"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":"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":"2504.12970","title":"MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection","created":"2025-04-17","cited_by_count":1,"area":null},{"id":"2510.12686","title":"Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories","created":"2025-10-14","cited_by_count":1,"area":null},{"id":"2602.06031","title":"AP-OOD: Attention Pooling for Out-of-Distribution Detection","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2502.03876","title":"Position: Untrained Machine Learning for Anomaly Detection by using 3D Point Cloud Data","created":"2025-02-06","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":"2510.02014","title":"Normality Calibration in Semi-supervised Graph Anomaly Detection","created":"2025-10-02","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":"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":"2608.24937","title":"Multi-Modal Anomaly Detection: A Survey","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2609.22136","title":"DiFA: Dual Evidence Fusion and Aggregation for Token-Level Text Anomaly Detection","created":"2026-08-24","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}],"next_cursor":null}