T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation

Hyperparameter Transfer in Graph Neural Networks

A transfer parameterization is developed and validated for GNNs trained with SGD, Adam, and AdamW that yields stable feature updates, learning rate transfer, and improved performance as width and depth increase, and provides a practical recipe for scaling GNNs across a variety of learning tasks and training scenarios.

Test-Time Adaptation for Graph Learning: A Systematic Survey

GATTA: Graph Active Learning with Test-Time Augmentation

Test-time augmentation (TTA) has proven effective for improving model robustness and uncertainty estimation in computer vision, yet its application to graph-structured data remains largely unexplored. We introduce GATTA (Graph Active Learning with Test-Time Augmentation), a framework for enhancing active learning by aggregating predictions across multiple augmented views to produce more reliable uncertainty estimates. To address the challenge of label-preserving graph augmentations, GATTA incorporates a consistency-based filtering mechanism that discards augmented views yielding unreliable predictions. We systematically evaluate GATTA across multiple graph datasets, GNN architectures, and acquisition strategies. Our results show that simple uncertainty-based methods, such as Entropy and Least Confidence, benefit most from TTA, achieving performance competitive with more sophisticated and computationally expensive approaches. GATTA generalizes across architectures, outperforms model-side ensemble methods such as MC Dropout. We further show that GATTA scales efficiently with both ensemble size and graph size. Extensive analysis of augmentation types, strengths, and filtering strategies provides practical guidelines for effective deployment. Our findings demonstrate that augmenting simple methods with TTA offers a more efficient path to strong active learning performance than engineering complex acquisition functions, enabling practitioners to achieve competitive results with lower computational overhead and reduced implementation complexity.

Taking lessons from history: Memory Augmented Self Distillation for graph neural networks

This study introduces a novel memory-augmented self-learning framework that extracts and provides diverse learning sources for adaptive knowledge distillation from the student model itself, resulting in a 2.5-6% increase in accuracy across various benchmark datasets compared to current GNN training and self-distillation methods.

SETTA: Parameter-Free Test-Time Adaptation for Graph Neural Networks via Spectral-Energy-Guided Semantic Refinement

Node classification is a central graph data mining task, yet repeated message passing can over-smooth representations and degrade frozen graph neural network (GNN) predictions after deployment. We present SETTA (Spectral-Energy Test-Time Adaptation), a prediction-level graph test-time adaptation framework that refines frozen outputs without test labels, gradients, parameter updates, or learnable adaptation parameters. SETTA denoises features for semantic-neighbor construction, adds complementary semantic routes while preserving observed edges, monitors a smoothness-energy proxy during diffusion, and accepts refinements through entropy-based gating. Configurations are fixed by a dataset-level protocol or selected using validation data only. Across six mostly homophilic benchmarks with 2708–19,717 nodes, SETTA improved a frozen two-layer GCN on every dataset and achieved the highest mean accuracy among the evaluated methods on five, with gains of 4.61, 3.08, and 2.01 percentage points on Cora, CiteSeer, and PubMed, respectively. Positive mean gains were also observed across all 30 dataset–backbone settings. Ablations and transition analyses indicate that semantic injection is most beneficial on sparse citation graphs and that selective refinement limits harmful changes. The current dense implementation supports benchmark-scale, amortized refinement; scalability and robustness on heterophilic graphs remain open.

Beyond Graph Distribution Shifts: LLMs, Adaptation, and Generalization

Graph machine learning has witnessed rapid progress across both academia and industry. However, most existing methods are developed under the in-distribution (I.D.) hypothesis, which assumes that training and testing graph data are drawn from the same distribution. In real-world applications—ranging from dynamic knowledge graphs to evolving biomedical networks—this assumption is frequently violated, resulting in severe performance degradation under distribution shifts. Addressing this challenge has become a key focus in recent years, leading to the development of novel paradigms that move beyond the I.D. setting. This tutorial presents a comprehensive overview of three emerging and synergistic directions for tackling distribution shifts in graph learning. First, we highlight Graph LLMs, which combine the representational power of large language models with graph structures to enable flexible, in-context, and few-shot learning on graphs. Second, we introduce adaptation techniques for both GNNs and Graph LLMs, including graph neural architecture search and continual learning strategies for evolving data. Third, we cover generalization methods that incorporate causality and invariance principles to build robust graph models under unseen distributions. We will advocate novel, high-quality research findings, as well as innovative solutions to the challenging problems in graph machine learning under distribution shifts and the applications on graphs. This topic is at the core of the scope of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, and is attractive to machine learning as well as data mining audience from both academia and industry.