Beyond Graph Distribution Shifts: LLMs, Adaptation, and Generalization

Test-Time Adaptation for Graph Learning: A Systematic Survey

Node4All: Learning Node Representation Beyond Datasets

This work introduces Node4All, a node representation learner applicable to arbitrary graph datasets without any dataset-specific optimization, and introduces the Channel Graph Transformer (CGT), which enables a single fixed parameterization to process arbitrary graph datasets.

Toward Generalist Models for Structured Data: Fundamentals, Emerging Trends and Applications

Structured data such as tabular data, time series and graphs powers many core data mining applications including recommendation, forecasting and user behavior analysis. Conventional approaches such as statistical models, classical machine learning methods and deep neural networks have achieved strong results. Yet most methods are designed for a single task or dataset and lack the ability to generalize across diverse structured data problems. Recent advances in foundation models point to a new direction for structured data modeling. Inspired by progress in natural language processing and computer vision, emerging research explores large-scale pretraining, synthetic data generation and in-context learning (ICL) to build more general-purpose models. In particular, tabular foundation models provide a promising path toward unifying heterogeneous structured modalities. This perspective treats tables as a common representation that can capture information from tabular data, time series, and graphs within a shared learning framework. Early studies show encouraging capabilities including cross-task generalization, few-shot adaptation and knowledge transfer across datasets. This tutorial presents a systematic overview of this emerging paradigm. We review classical foundations, introduce recent tabular foundation models, and discuss key challenges in pretraining data generation, model design, and multi-task learning.

HGOOD : Hypergraph-enhanced Graph Contrastive Learning for Graph Out-of-Distribution Detection

A Hypergraph-enhanced graph contrastive learning framework for Graph Out-Of-Distribution detection (termed HGOOD), which constructs two branches to hierarchically mine graph compact semantics in a comprehensive manner and introduces a cross-branch prototype contrast that aligns the captured graph patterns with their cross-branch clustering prototypes to enhance the semantic manifold of the in-distribution graph.

From topology learning to graph generation: A unifying perspective

Learning graph structures from data is a fundamental problem that spans a wide range of signal processing and machine learning tasks. While significant effort has been made to tackle the problem, existing research has largely evolved along two parallel directions. The first seeks to infer the topology of an individual graph from observations supported on it, whereas the second seeks to learn a generative distribution from observed graph instances, enabling the sampling of new graphs. This review presents a unified framework that connects these formulations by viewing them as inverse problems of a common generation process for graph data. We review the major methodologies within this framework, highlight their relationships, strengths, and limitations, and identify opportunities for integrating ideas across paradigms. By bridging graph topology learning and graph generation, this review provides a broader cross-disciplinary perspective on the field and outlines promising directions for future research.

2nd Workshop on Frontiers in Graph Machine Learning for the Large Model Era

The "2nd Frontiers in Graph Machine Learning for the Large Model Era (GMLLM'26)" workshop focuses on advancing graph machine learning (GML) techniques in the context of large-scale foundation models. Graphs offer a principled way to represent structured and relational data, making them essential for capturing complex dependencies in knowledge, systems, and behaviors. As the scale and influence of foundation models grow, graph learning is well positioned to enhance model robustness, improve interpretability, and integrate domain-specific relational priors. This workshop explores how graph learning can support emerging challenges in knowledge reasoning, temporal and multi-hop inference, and AI systems. It also investigates how advances in representation learning, structure-aware generalization, and efficient graph processing can contribute to trustworthy and scalable AI systems. By convening experts in graph learning, knowledge management, and LLMs, the workshop aims to identify core challenges and opportunities of GML in the large model era.