Semi-supervised Graph Anomaly Detection via Robust Homophily Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Ai, Guoguo, Qiao, Hezhe, Yan, Hui, Pang, Guansong |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Normality Calibration in Semi-supervised Graph Anomaly Detection
by: Zeng, Guolei, et al.
Published: (2025)
by: Zeng, Guolei, et al.
Published: (2025)
Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection
by: Qiao, Hezhe, et al.
Published: (2023)
by: Qiao, Hezhe, et al.
Published: (2023)
Generative Semi-supervised Graph Anomaly Detection
by: Qiao, Hezhe, et al.
Published: (2024)
by: Qiao, Hezhe, et al.
Published: (2024)
GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
by: Ai, Guoguo, et al.
Published: (2024)
by: Ai, Guoguo, et al.
Published: (2024)
AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection
by: Qiao, Hezhe, et al.
Published: (2025)
by: Qiao, Hezhe, et al.
Published: (2025)
Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts
by: Niu, Chaoxi, et al.
Published: (2024)
by: Niu, Chaoxi, et al.
Published: (2024)
TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection
by: He, Hui, et al.
Published: (2026)
by: He, Hui, et al.
Published: (2026)
Deep Graph Anomaly Detection: A Survey and New Perspectives
by: Qiao, Hezhe, et al.
Published: (2024)
by: Qiao, Hezhe, et al.
Published: (2024)
Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
by: Chen, Yutong, et al.
Published: (2024)
by: Chen, Yutong, et al.
Published: (2024)
Toward Robust Semi-supervised Regression via Dual-stream Knowledge Distillation
by: Su, Ye, et al.
Published: (2025)
by: Su, Ye, et al.
Published: (2025)
Robust Hallucination Detection in LLMs via Adaptive Token Selection
by: Niu, Mengjia, et al.
Published: (2025)
by: Niu, Mengjia, et al.
Published: (2025)
Open-Set Graph Anomaly Detection via Normal Structure Regularisation
by: Wang, Qizhou, et al.
Published: (2023)
by: Wang, Qizhou, et al.
Published: (2023)
Adaptive Deviation Learning for Visual Anomaly Detection with Data Contamination
by: Das, Anindya Sundar, et al.
Published: (2024)
by: Das, Anindya Sundar, et al.
Published: (2024)
Semi-supervised Domain Adaptation in Graph Transfer Learning
by: Qiao, Ziyue, et al.
Published: (2023)
by: Qiao, Ziyue, et al.
Published: (2023)
Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs
by: Chen, Jiazhen, et al.
Published: (2025)
by: Chen, Jiazhen, et al.
Published: (2025)
Adapting Large Language Models for Parameter-Efficient Log Anomaly Detection
by: Lim, Ying Fu, et al.
Published: (2025)
by: Lim, Ying Fu, et al.
Published: (2025)
Graph Continual Learning with Debiased Lossless Memory Replay
by: Niu, Chaoxi, et al.
Published: (2024)
by: Niu, Chaoxi, et al.
Published: (2024)
Mitigating Homophily Disparity in Graph Anomaly Detection: A Scalable and Adaptive Approach
by: Liu, Yunhui, et al.
Published: (2026)
by: Liu, Yunhui, et al.
Published: (2026)
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification
by: Sun, Henan, et al.
Published: (2023)
by: Sun, Henan, et al.
Published: (2023)
AnomalyAID: Reliable Interpretation for Semi-supervised Network Anomaly Detection
by: Yuan, Yachao, et al.
Published: (2024)
by: Yuan, Yachao, et al.
Published: (2024)
Towards Robust Graph Structural Learning Beyond Homophily via Preserving Neighbor Similarity
by: Zhu, Yulin, et al.
Published: (2024)
by: Zhu, Yulin, et al.
Published: (2024)
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks
by: Ma, Rongrong, et al.
Published: (2024)
by: Ma, Rongrong, et al.
Published: (2024)
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
by: Zhang, Rui, et al.
Published: (2024)
by: Zhang, Rui, et al.
Published: (2024)
Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning
by: Niu, Chaoxi, et al.
Published: (2023)
by: Niu, Chaoxi, et al.
Published: (2023)
Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation
by: Huang, Wei, et al.
Published: (2026)
by: Huang, Wei, et al.
Published: (2026)
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks
by: Ma, Rongrong, et al.
Published: (2024)
by: Ma, Rongrong, et al.
Published: (2024)
Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection
by: Chen, Feiyi, et al.
Published: (2025)
by: Chen, Feiyi, et al.
Published: (2025)
Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach
by: Niu, Chaoxi, et al.
Published: (2024)
by: Niu, Chaoxi, et al.
Published: (2024)
Homophily-aware Heterogeneous Graph Contrastive Learning
by: Wang, Haosen, et al.
Published: (2025)
by: Wang, Haosen, et al.
Published: (2025)
When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
by: Huang, Wei, et al.
Published: (2026)
by: Huang, Wei, et al.
Published: (2026)
Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification
by: Ai, Wei, et al.
Published: (2024)
by: Ai, Wei, et al.
Published: (2024)
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
by: Xu, Hongzuo, et al.
Published: (2022)
by: Xu, Hongzuo, et al.
Published: (2022)
IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection
by: Zhou, Xiaohui, et al.
Published: (2026)
by: Zhou, Xiaohui, et al.
Published: (2026)
Comparative Study on Semi-supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System
by: Dong, Yongqi, et al.
Published: (2023)
by: Dong, Yongqi, et al.
Published: (2023)
Cross-Domain Graph Anomaly Detection via Test-Time Training with Homophily-Guided Self-Supervision
by: Pirhayati, Delaram, et al.
Published: (2025)
by: Pirhayati, Delaram, et al.
Published: (2025)
Domain-Skewed Federated Learning with Feature Decoupling and Calibration
by: Wang, Huan, et al.
Published: (2026)
by: Wang, Huan, et al.
Published: (2026)
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals
by: Chen, Xiangwei, et al.
Published: (2024)
by: Chen, Xiangwei, et al.
Published: (2024)
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
by: Chen, Feiyi, et al.
Published: (2023)
by: Chen, Feiyi, et al.
Published: (2023)
What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks
by: Zheng, Yilun, et al.
Published: (2024)
by: Zheng, Yilun, et al.
Published: (2024)
Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network
by: Dong, Yongqi, et al.
Published: (2022)
by: Dong, Yongqi, et al.
Published: (2022)
Similar Items
-
Normality Calibration in Semi-supervised Graph Anomaly Detection
by: Zeng, Guolei, et al.
Published: (2025) -
Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection
by: Qiao, Hezhe, et al.
Published: (2023) -
Generative Semi-supervised Graph Anomaly Detection
by: Qiao, Hezhe, et al.
Published: (2024) -
GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
by: Ai, Guoguo, et al.
Published: (2024) -
AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection
by: Qiao, Hezhe, et al.
Published: (2025)