只标注1%节点就能达到顶尖模型效果，大幅降低文本图学习成本
Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment
- 精选代表性节点和边进行标注，构建小规模注释图
- 通过双层对齐模块将注释图与原图结构融合，实现高效信息传递
- 仅需1%标注数据即可媲美甚至超越现有方法，适合资源受限场景
在文本属性图（TAGs）中，传统图神经网络因节点文本信息复杂而表现不佳。尽管近期方法利用大语言模型（LLMs）增强节点文本特征，但通常需对所有节点进行大量标注或微调，代价高昂。为此，本文提出GAGA框架，通过仅标注代表性节点和边来降低标注成本，构建捕捉拓扑关系的注释图，并引入两层对齐模块，有效融合注释图与原始图的结构。实验表明，GAGA在分类准确率上达到或超过当前最优方法，且仅需1%的数据标注，展现出显著效率优势。
原文摘要 · Abstract (English)
In the realm of Text-attributed Graphs (TAGs), traditional graph neural networks (GNNs) often fall short due to the complex textual information associated with each node. Recent methods have improved node representations by leveraging large language models (LLMs) to enhance node text features, but these approaches typically require extensive annotations or fine-tuning across all nodes, which is both time-consuming and costly. To overcome these challenges, we introduce GAGA, an efficient framework for TAG representation learning. GAGA reduces annotation time and cost by focusing on annotating only representative nodes and edges. It constructs an annotation graph that captures the topological relationships among these annotations. Furthermore, GAGA employs a two-level alignment module to effectively integrate the annotation graph with the TAG, aligning their underlying structures. Experiments show that GAGA achieves classification accuracies on par with or surpassing state-of-the-art methods while requiring only 1% of the data to be annotated, demonstrating its high efficiency.
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