English(EN) Task-Specific Prompt with Global Context for Multi-Task Graph Pre-Training

新的TPGC方法增强了低资源场景下的多任务图预训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了TPGC，一种新颖的多任务图预训练方法，可增强提示表示的相关性和可迁移性。该方法采用双先验提示初始化策略，结合任务和结构先验，以改善低资源场景下的性能。在六个基准测试上的实验表明，TPGC在少样本设置下取得了优越的结果，与现有方法相比，需要更少的可调参数和更少的运行时。 AI

影响这种新方法可以提高数据有限场景下图神经网络的训练效率和有效性。

排序理由该集群包含一篇详细介绍图预训练新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Zhiyang Qiu, Yangtao Wang, Xiaocui Li, Yanzhao Xie, Siyuan Chen, Wensheng Zhang · 2026-09-02 04:00

面向多任务图预训练的任务特定提示与全局上下文

arXiv:2609.00047v1 Announce Type: cross Abstract: Graph prompt learning is an effective paradigm to adapt pre-trained graph models to downstream tasks in low-resource scenarios. However, existing multi-task graph pre-training frameworks generally use randomly initialized prompts,…