超越单一粒度提示：用于图的多尺度链式思考提示学习
Beyond Single-Granularity Prompts: A Multi-Scale Chain-of-Thought Prompt Learning for Graph
AI总结：
本文提出多尺度图链式思考提示框架，通过多尺度信息增强图提示学习，提升少样本场景下的性能。
摘要：
『预训练、提示』范式旨在弥合预训练任务与下游目标之间的差距，已从NLP领域扩展到图领域并取得了显著进展。当前主流图提示微调方法通过可学习的提示向量修改输入或输出特征。然而，现有方法在生成提示时局限于单一粒度（例如节点级或子图级），忽视了图数据中固有的多尺度结构信息，限制了提示语义的多样性。为了解决这一问题，我们率先将多尺度信息整合到图提示中，并提出多尺度图链式思考（MSGCOT）提示框架。具体而言，我们设计了一个轻量级、低秩粗化网络，以高效捕捉多尺度结构特征作为层次化基向量用于提示生成。随后，模仿人类从粗到细的推理过程，我们在每个推理步骤中动态整合多尺度信息，形成渐进式的粗到细提示链。在八个基准数据集上的广泛实验表明，MSGCOT优于最先进的单一粒度图提示微调方法，特别是在少样本场景中表现出优越的性能。代码可在：https://github.com/zhengziyu77/MSGCOT 上获得。
英文摘要：
The ``pre-train, prompt" paradigm, designed to bridge the gap between pre-training tasks and downstream objectives, has been extended from the NLP domain to the graph domain and has achieved remarkable progress. Current mainstream graph prompt-tuning methods modify input or output features using learnable prompt vectors. However, existing approaches are confined to single-granularity (e.g., node-level or subgraph-level) during prompt generation, overlooking the inherently multi-scale structural information in graph data, which limits the diversity of prompt semantics. To address this issue, we pioneer the integration of multi-scale information into graph prompt and propose a Multi-Scale Graph Chain-of-Thought (MSGCOT) prompting framework. Specifically, we design a lightweight, low-rank coarsening network to efficiently capture multi-scale structural features as hierarchical basis vectors for prompt generation. Subsequently, mimicking human cognition from coarse-to-fine granularity, we dynamically integrate multi-scale information at each reasoning step, forming a progressive coarse-to-fine prompt chain. Extensive experiments on eight benchmark datasets demonstrate that MSGCOT outperforms the state-of-the-art single-granularity graph prompt-tuning method, particularly in few-shot scenarios, showcasing superior performance. The code is available at: https://github.com/zhengziyu77/MSGCOT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
