Image de couverture de livre

Online GNN Evaluation Under Test-time Graph Distribution Shifts

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zheng, Xin, Song, Dongjin, Wen, Qingsong, Du, Bo, Pan, Shirui
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Test-time GNN Model Evaluation on Dynamic Graphs
par: Li, Bo, et autres
Publié: (2025)

Foundation Models for Time Series Analysis: A Tutorial and Survey
par: Liang, Yuxuan, et autres
Publié: (2024)

DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts
par: Sun, Guiquan, et autres
Publié: (2026)

Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
par: Wu, Man, et autres
Publié: (2024)

GOODAT: Towards Test-time Graph Out-of-Distribution Detection
par: Wang, Luzhi, et autres
Publié: (2024)

AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
par: Lin, Xixun, et autres
Publié: (2026)

ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
par: Chen, Hongjiang, et autres
Publié: (2026)

LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
par: Pan, Sheng, et autres
Publié: (2026)

Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting
par: Cui, Zhiqing, et autres
Publié: (2026)

Distribution Matching for Graph Quantification Under Structural Covariate Shift
par: Damke, Clemens, et autres
Publié: (2025)

INSPIRE-GNN: Intelligent Sensor Placement to Improve Sparse Bicycling Network Prediction via Reinforcement Learning Boosted Graph Neural Networks
par: Gupta, Mohit, et autres
Publié: (2025)

Out-of-Distribution Detection on Graphs: A Survey
par: Cai, Tingyi, et autres
Publié: (2025)

Causal Imitation Learning Under Measurement Error and Distribution Shift
par: Bo, Shi, et autres
Publié: (2026)

A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection
par: Pan, Junjun, et autres
Publié: (2025)

Robust Anomaly Detection Under Normality Distribution Shift in Dynamic Graphs
par: Xu, Xiaoyang, et autres
Publié: (2025)

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
par: Zhang, Kexin, et autres
Publié: (2023)

Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time
par: Pan, Junjun, et autres
Publié: (2025)

Graph Neural Networks for Graphs with Heterophily: A Survey
par: Zheng, Xin, et autres
Publié: (2022)

A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
par: Zhang, Kexin, et autres
Publié: (2024)

Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
par: Jin, Ming, et autres
Publié: (2023)

Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models
par: Shen, Xu, et autres
Publié: (2024)

Continual Learning on Graphs: Challenges, Solutions, and Opportunities
par: Zhang, Xikun, et autres
Publié: (2024)

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
par: Jin, Ming, et autres
Publié: (2023)

Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt
par: Zhang, YiFan, et autres
Publié: (2024)

Benchmarks and Custom Package for Energy Forecasting
par: Wang, Zhixian, et autres
Publié: (2023)

Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
par: Zhang, Yuxiang, et autres
Publié: (2024)

Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts
par: Qin, Dalin, et autres
Publié: (2024)

Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark
par: Wang, Yili, et autres
Publié: (2024)

Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
par: Zhang, Zeyang, et autres
Publié: (2024)

MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts
par: Xie, Renchunzi, et autres
Publié: (2024)

All Nodes are created Not Equal: Node-Specific Layer Aggregation and Filtration for GNN
par: Wang, Shilong, et autres
Publié: (2024)

Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach
par: Gupta, Mohit, et autres
Publié: (2024)

Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
par: Zheng, Weihuang, et autres
Publié: (2024)

HERO: Heterogeneous Continual Graph Learning via Meta-Knowledge Distillation
par: Sun, Guiquan, et autres
Publié: (2025)

Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
par: Zhang, He, et autres
Publié: (2023)

Testing For Distribution Shifts with Conditional Conformal Test Martingales
par: Shaer, Shalev, et autres
Publié: (2026)

When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift
par: Fomin, Max
Publié: (2026)

ARC: A Generalist Graph Anomaly Detector with In-Context Learning
par: Liu, Yixin, et autres
Publié: (2024)

Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification
par: Liang, Langzhang, et autres
Publié: (2025)

SVTime: Small Time Series Forecasting Models Informed by "Physics" of Large Vision Model Forecasters
par: Shen, ChengAo, et autres
Publié: (2025)