Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach
Fuente:
arXiv
Saved in:
| Main Authors: | Chen, Qingfeng, Li, Shiyuan, Liu, Yixin, Pan, Shirui, Webb, Geoffrey I., Zhang, Shichao |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation
by: Li, Shiyuan, et al.
Published: (2024)
by: Li, Shiyuan, et al.
Published: (2024)
FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
by: Zhao, Yunfeng, et al.
Published: (2026)
by: Zhao, Yunfeng, et al.
Published: (2026)
FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
by: Zhao, Yunfeng, et al.
Published: (2025)
by: Zhao, Yunfeng, et al.
Published: (2025)
ARC: A Generalist Graph Anomaly Detector with In-Context Learning
by: Liu, Yixin, et al.
Published: (2024)
by: Liu, Yixin, et al.
Published: (2024)
Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations
by: Liu, Yixin, et al.
Published: (2026)
by: Liu, Yixin, et al.
Published: (2026)
From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection
by: Liu, Yixin, et al.
Published: (2026)
by: Liu, Yixin, et al.
Published: (2026)
Graph Neural Networks for Graphs with Heterophily: A Survey
by: Zheng, Xin, et al.
Published: (2022)
by: Zheng, Xin, et al.
Published: (2022)
OFA-MAS: One-for-All Multi-Agent System Topology Design based on Mixture-of-Experts Graph Generative Models
by: Li, Shiyuan, et al.
Published: (2026)
by: Li, Shiyuan, et al.
Published: (2026)
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
by: Jin, Ming, et al.
Published: (2023)
by: Jin, Ming, et al.
Published: (2023)
AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
by: Lin, Xixun, et al.
Published: (2026)
by: Lin, Xixun, et al.
Published: (2026)
Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
by: Zhang, He, et al.
Published: (2023)
by: Zhang, He, et al.
Published: (2023)
Towards One-for-All Anomaly Detection for Tabular Data
by: Li, Shiyuan, et al.
Published: (2026)
by: Li, Shiyuan, et al.
Published: (2026)
Decision-focused Graph Neural Networks for Combinatorial Optimization
by: Liu, Yang, et al.
Published: (2024)
by: Liu, Yang, et al.
Published: (2024)
Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models
by: Wang, Zhibiao, et al.
Published: (2025)
by: Wang, Zhibiao, et al.
Published: (2025)
A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection
by: Pan, Junjun, et al.
Published: (2025)
by: Pan, Junjun, et al.
Published: (2025)
Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach
by: Liu, Yujing, et al.
Published: (2026)
by: Liu, Yujing, et al.
Published: (2026)
Out-of-Distribution Detection on Graphs: A Survey
by: Cai, Tingyi, et al.
Published: (2025)
by: Cai, Tingyi, et al.
Published: (2025)
Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach
by: Gupta, Mohit, et al.
Published: (2024)
by: Gupta, Mohit, et al.
Published: (2024)
DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection
by: Chen, Qingfeng, et al.
Published: (2025)
by: Chen, Qingfeng, et al.
Published: (2025)
Deep Learning for Time Series Anomaly Detection: A Survey
by: Darban, Zahra Zamanzadeh, et al.
Published: (2022)
by: Darban, Zahra Zamanzadeh, et al.
Published: (2022)
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
by: Zhang, He, et al.
Published: (2022)
by: Zhang, He, et al.
Published: (2022)
GenIAS: Generator for Instantiating Anomalies in time Series
by: Darban, Zahra Zamanzadeh, et al.
Published: (2025)
by: Darban, Zahra Zamanzadeh, et al.
Published: (2025)
GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems
by: Chen, Hongjiang, et al.
Published: (2026)
by: Chen, Hongjiang, et al.
Published: (2026)
Hierarchical Uncertainty-Aware Graph Neural Network
by: Choi, Yoonhyuk, et al.
Published: (2025)
by: Choi, Yoonhyuk, et al.
Published: (2025)
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection
by: Darban, Zahra Zamanzadeh, et al.
Published: (2023)
by: Darban, Zahra Zamanzadeh, et al.
Published: (2023)
Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification
by: Liang, Langzhang, et al.
Published: (2025)
by: Liang, Langzhang, et al.
Published: (2025)
Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time
by: Pan, Junjun, et al.
Published: (2025)
by: Pan, Junjun, et al.
Published: (2025)
CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection
by: Pan, Junjun, et al.
Published: (2026)
by: Pan, Junjun, et al.
Published: (2026)
Towards the Next-generation Bayesian Network Classifiers
by: Zhang, Huan, et al.
Published: (2025)
by: Zhang, Huan, et al.
Published: (2025)
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation
by: Li, Chengyu, et al.
Published: (2024)
by: Li, Chengyu, et al.
Published: (2024)
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
by: Liu, Xin, et al.
Published: (2024)
by: Liu, Xin, et al.
Published: (2024)
Evidential Uncertainty Probes for Graph Neural Networks
by: Yu, Linlin, et al.
Published: (2025)
by: Yu, Linlin, et al.
Published: (2025)
Multi-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detection
by: Lv, Jiaqi, et al.
Published: (2026)
by: Lv, Jiaqi, et al.
Published: (2026)
Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick
by: Wang, Xiyuan, et al.
Published: (2023)
by: Wang, Xiyuan, et al.
Published: (2023)
MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Prediction
by: Yang, Yunchi, et al.
Published: (2026)
by: Yang, Yunchi, et al.
Published: (2026)
Uncertainty in Graph Neural Networks: A Survey
by: Wang, Fangxin, et al.
Published: (2024)
by: Wang, Fangxin, et al.
Published: (2024)
Variational Bayesian Flow Network for Graph Generation
by: Xiong, Yida, et al.
Published: (2026)
by: Xiong, Yida, et al.
Published: (2026)
Hyperparameter Transfer Laws for Non-Recurrent Multi-Path Neural Networks
by: Wu, Shenxi, et al.
Published: (2026)
by: Wu, Shenxi, et al.
Published: (2026)
Neural Network Graph Similarity Computation Based on Graph Fusion
by: Chang, Zenghui, et al.
Published: (2025)
by: Chang, Zenghui, et al.
Published: (2025)
Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
by: Feng, Jiarui, et al.
Published: (2023)
by: Feng, Jiarui, et al.
Published: (2023)
Similar Items
-
Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation
by: Li, Shiyuan, et al.
Published: (2024) -
FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
by: Zhao, Yunfeng, et al.
Published: (2026) -
FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
by: Zhao, Yunfeng, et al.
Published: (2025) -
ARC: A Generalist Graph Anomaly Detector with In-Context Learning
by: Liu, Yixin, et al.
Published: (2024) -
Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations
by: Liu, Yixin, et al.
Published: (2026)