面向多标签图基础模型：从单向量表示学习到多语义基学习

中文摘要 AI 辅助

多标签节点分类是图学习中一项重要且具有挑战性的任务，其中节点同时呈现多种语义。现有的多标签节点分类方法能够有效建模多个标签，但仅考虑模型需在同一图域内进行训练和测试的域内场景，导致跨域泛化能力有限。近年来，图基础模型（Graph Foundation Models, GFMs）已成为学习适用于不同图域和下游任务的可迁移图表示的有前景范式。然而，现有的图基础模型构建于单标签假设之上，即所有节点被任意视为仅包含一类语义并嵌入到单一表示中。对于多标签节点，此类表示本质上用表示空间中的单个点近似多种语义，不可避免地导致语义纠缠，难以同时区分多个标签。为解决这些局限，我们提出了多语义基图基础模型（Multi-Semantic Basis Graph Foundation Model, MSB-GFM），这是一个用于跨域多标签节点分类的框架。具体而言，我们引入多语义基表示学习范式，将每个多标签节点建模为语义基的自适应组合，从而具备灵活的表示能力以建模多种语义。此外，我们开发了带有域对抗训练的语义-结构双通道架构，以实现有效的跨域知识迁移。大量实验验证了我们模型的有效性。

英文摘要

Multi-label node classification is an important yet challenging task in graph learning, where nodes exhibit multiple semantics simultaneously. Existing methods for multi-label node classification can effectively model multiple labels, while only considering in-domain scenarios where the model needs to be trained and tested within the same graph domain, resulting in limited cross-domain generalization. Recently, Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable graph representations across diverse graph domains and downstream tasks. However, existing GFMs are built upon single-label assumption, where all nodes are arbitrarily regarded as containing only one class of semantic and embedded into a single representation. For multi-label nodes, such a representation essentially approximates multiple semantics with a single point in the representation space, inevitably leading to semantic entanglement and making simultaneous discrimination of multiple labels difficult. To address these limitations, we propose a Multi-Semantic Basis Graph Foundation Model (MSB-GFM), a framework for cross-domain multi-label node classification. Specifically, we introduce a multi-semantic basis representation learning paradigm that models each multi-label node as an adaptive composition of semantic bases, thereby enabling flexible representational capacity for modeling multiple semantics. Furthermore, we develop a semantic-structure dual-channel architecture with domain adversarial training for effective cross-domain knowledge transfer. Extensive experiments demonstrate the effectiveness of our model.

发表机构

• Tianjin University（天津大学）

机构由 AI 辅助整理，请以论文原文为准。