提出混合谱方法，让图预训练更好适配不同图结构。
Aligning the Spectrum: Hybrid Graph Pre-training and Prompt Tuning across Homophily and Heterophily
- 用混合谱骨干网络构建更丰富的知识基础。
- 在同质和异质图上均实现更高精度，超越单滤波器方法。
- 适合需要高效迁移学习的图分析任务。
图的‘预训练与提示调优’通过对齐下游任务与预训练目标，实现在少量标注下的高效知识迁移。然而现有方法多依赖单一滤波器（如低通），而真实图数据具有固有的谱多样性。我们的理论‘谱特异性’原则表明，有效知识迁移需预训练谱滤波器与下游图的内在谱特性对齐。这揭示了两个根本局限：(1) 知识瓶颈：单滤波器模型会不可逆地丢失其他频段信号（如高频）；(2) 利用瓶颈：预训练滤波器与下游谱不匹配，导致预训练知识严重浪费。为此，我们提出HS-GPPT。采用混合谱骨干网络构建丰富知识基底，并引入谱对齐提示调优，主动将下游图谱与多样预训练滤波器对齐，从而在同质与异质图中全面利用知识。大量实验验证其在归纳与直推式学习设置下的有效性。
原文摘要 · Abstract (English)
Graph ``pre-training and prompt-tuning'' aligns downstream tasks with pre-trained objectives to enable efficient knowledge transfer under limited supervision. However, current methods typically rely on single-filter backbones (e.g., low-pass), whereas real-world graphs exhibit inherent spectral diversity. Our theoretical \textit{Spectral Specificity} principle reveals that effective knowledge transfer requires alignment between pre-trained spectral filters and the intrinsic spectrum of downstream graphs. This identifies two fundamental limitations: (1) Knowledge Bottleneck: single-filter models suffer from irreversible information loss by suppressing signals from other frequency bands (e.g., high-frequency); (2) Utilization Bottleneck: spectral mismatches between pre-trained filters and downstream spectra lead to significant underutilization of pre-trained knowledge. To bridge this gap, we propose HS-GPPT. We utilize a hybrid spectral backbone to construct an abundant knowledge basis. Crucially, we introduce Spectral-Aligned Prompt Tuning to actively align the downstream graph's spectrum with diverse pre-trained filters, facilitating comprehensive knowledge utilization across both homophily and heterophily. Extensive experiments validate the effectiveness under both transductive and inductive learning settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。