发表机构
University of Toronto（多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Scout框架，通过任务效用监督学习动态网络中节点查询价值，在有限预算下有效分配观测资源，实验表明任务匹配获取显著提升下游性能。
AI 中文摘要
当底层网络的变化仅被部分观测到时，在动态图上的学习是困难的。获取当前的图信息会产生观测和计算成本，因此在有限资源下，完全更新是不切实际的。本文关注动态网络上的预算约束下任务感知获取问题，其中模型需要决定为下游任务刷新哪些过时的图信息。我们提出了Scout，一个轻量级框架，它从维护的图和观测历史中学习查询每个节点的任务价值。我们的评估涵盖了一个合成和四个真实世界的动态网络、两个下游任务、九个获取基线和多个查询预算。Scout在21个基准-预算设置中的19个中取得了最高的平均下游性能。任务效用监督在16个真实世界设置中的13个中也优于结构变化监督。在相同的动态网络上，任务匹配的获取相比任务不匹配的获取，在链接预测AUC上提高了0.012-0.016，在节点分类准确率上提高了0.064-0.09。这些结果表明，有用的图观测取决于下游任务，并且通过直接学习下游效用，可以更有效地分配有限的观测预算。
英文摘要
Learning on dynamic graphs is difficult when changes in the underlying network are only partially observed. Acquiring current graph information incurs observation and computational costs, making complete updates impractical under limited resources. This paper focuses on budgeted task-aware acquisition on dynamic networks, where a model needs to decide which stale graph information to refresh for a downstream task. We propose Scout, a lightweight framework that learns the task value of querying each node from the maintained graph and observation history. Our evaluation covers one synthetic and four real-world dynamic networks, two downstream tasks, nine acquisition baselines, and several query budgets. Scout achieves the highest mean downstream performance in 19 of the 21 benchmark-budget settings. Task-utility supervision also outperforms structural-change supervision in 13 of the 16 real-world settings. On the same dynamic network, task-matched acquisition improves link-prediction AUC by 0.012-0.016 and node-classification accuracy by 0.064-0.09 over task-mismatched acquisition. These results show that useful graph observations depend on the downstream task and that limited observation budgets can be allocated more effectively by learning directly from downstream utility.
