通过图到表对齐实现惊人简单且有效的多域图基础模型
Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment
- Nankai University（南开大学）
- Beihang University（北京航空航天大学）
- Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究如何让表格基础模型有效捕获图结构信息，提出GTAlign框架，先预训练图编码器捕获图表示，通过社区引导持续预训练弥合差距，应用于目标域推理，实验表明该框架在节点和图分类上显著优于基线，提供简单无文本的图基础模型。
AI中文摘要：
图基础模型（GFMs）已成为跨不同图域学习可转移表示的有前途的范式。GFMs的最新进展主要由图神经网络和基于大语言模型（LLM）的方法主导，但这些方法在有限数据训练和对文本属性的严重依赖之间面临根本困境。表格基础模型（TFMs）提供了一种潜在替代方案，但如何让TFMs有效捕获图的结构信息仍未得到充分探索。关键挑战是学习一种图到表对齐机制。为解决此问题，我们提出了GTAlign，一种用于无文本图基础模型的惊人简单而有效的图到表对齐框架。具体而言，我们首先预训练一个图编码器，将不同的图映射到统一的潜在空间以捕获与域无关的图表示。为进一步弥合图拓扑与表格表示空间之间的差距，我们提出社区引导的持续预训练，使用从图社区派生的伪标签来构建少样本预测情节。最后，我们将图编码器应用于未见的目标域并进行上下文推理。在五个基准数据集上的广泛实验表明，GTAlign在节点和图分类方面均显著优于现有基线，提供了一个简单、有效且无文本的GFM模型。代码将在接受后发布。
英文摘要：
Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains. Recent advancements in GFMs have been largely dominated by two paradigms: Graph Neural Network and Large Language Model (LLM) based methods. However, these methods often face a fundamental dilemma between training with limited data and a heavy reliance on textual attributes. Tabular foundation models (TFMs) offer a potential alternative, as node features and representations can be naturally organized in a tabular form. However, how to enable TFMs to effectively capture structural information of graphs remains largely unexplored. The key challenge is to learn a graph-to-table alignment mechanism that enables graph structural understanding for TFMs. To address this, we propose GTAlign, a surprisingly simple yet effective Graph-to-Table Alignment framework for text-free Graph Foundation Model. Specifically, we first pretrain a graph encoder that maps diverse graphs into a unified latent space to capture domain-agnostic graph representations. To further bridge the gap between graph topology and the tabular representation space, we propose community-guided continual pre-training, where pseudo-labels derived from graph community are used to construct few-shot prediction episodes. Lastly, we adapt the graph encoder for an unseen target domain and perform in-context inference. Extensive experiments on five benchmark datasets demonstrate that GTAlign significantly outperforms state-of-the-art baselines on both node and graph classification, offering a simple, effective, and text-free GFM model. Code will be released upon acceptance.
