基于拓扑路由的混合曲率专家的动态图提示
Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
AI总结：
本研究针对动态图提示的几何适配不足问题，提出CurvPrompt框架，通过拓扑路由混合曲率专家实现自适应表示，在少样本链接预测和节点分类任务上表现优异，验证了几何自适应提示的必要性。
AI中文摘要：
动态图提示会冻结预训练的时间主干网络，并利用轻量型提示将其适配到标签稀缺的下游任务中。然而，现有方法均在单一固定的嵌入空间内运行。本研究发现，局部聚类和度异质性的时间变化会主动重排边曲率谱，这表明最优表示几何会随时间随局部拓扑动态演化。我们将这种未被解决的不匹配问题形式化为几何适配不足。为克服该局限，我们提出了CurvPrompt，一种面向动态图的拓扑路由几何提示框架。CurvPrompt不依赖单一空间，而是维护一组曲率多样的黎曼专家，每个专家配对一个可学习提示。拓扑感知门控会动态将每个节点-时间实例路由到稀疏的专家子集，构建个性化混合曲率表示。为确保在极端标签稀缺下的参数效率和训练稳定性，CurvPrompt在预训练期间采用软路由构建连续的拓扑-几何映射，在下游适配期间切换为带均匀权重的硬Top-K路由。在四个基准数据集上的大量实验表明，CurvPrompt大幅提升了少样本链接预测性能，同时在节点分类任务上也展现出强劲且稳定的表现，验证了几何自适应提示的必要性。
英文摘要：
Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts. However, existing methods operate within a single, fixed embedding space. In this work, we reveal that temporal shifts in local clustering and degree heterogeneity actively reorganize the edge curvature spectrum---indicating that the optimal representation geometry dynamically evolves with local topology over time. We formalize this unaddressed mismatch as geometry under-adaptation. To overcome this limitation, we propose CurvPrompt, a topology-routed geometry prompting framework for dynamic graphs. Instead of relying on a single space, CurvPrompt maintains a bank of curvature-diverse Riemannian experts, each paired with a learnable prompt. A topology-aware gate dynamically routes each node--time instance to a sparse subset of experts, constructing a personalized mixed-curvature representation. To ensure parameter efficiency and training stability under extreme label scarcity, CurvPrompt employs soft routing during pre-training to build a continuous topology--geometry mapping, and transitions to hard Top-K routing with uniform weights during downstream adaptation. Extensive experiments across four benchmark datasets show that CurvPrompt significantly advances few-shot link prediction while delivering strong, consistent performance on node classification tasks, validating the necessity of geometry-adaptive prompting.
