Toward General and Robust LLM-enhanced Text-attributed Graph Learning

Recent advancements in Large Language Models (LLMs) and the proliferation of Text-Attributed Graphs (TAGs) across various domains have positioned LLM-enhanced TAG learning as a critical research area. However, the field faces significant challenges: (1) the absence of a unified framework to systematize the diverse optimization perspectives, and (2) the lack of a robust method capable of handling real-world TAGs, which often suffer from text and edge sparsity, leading to suboptimal performance. To address these challenges, we propose UltraTAG, a unified pipeline for LLM-enhanced TAG learning. UltraTAG provides a comprehensive and domain-adaptive framework that not only organizes existing methodologies but also paves the way for future advancements. Building on this framework, we propose UltraTAG-S, a robust instantiation designed to tackle sparsity issues in real-world TAGs. UltraTAG-S employs LLM-based text propagation and augmentation to mitigate text sparsity, while leveraging LLM-augmented node selection based on PageRank and edge reconfiguration strategies to address edge sparsity. Our experiments demonstrate UltraTAG-S significantly outperforms existing baselines, achieving improvements of 2.12% and 17.47% in ideal and sparse settings, respectively. Moreover, as the data sparsity ratio increases, the performance improvement of UltraTAG-S also rises.