English(EN) HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models

HOPPER框架通过可学习的跳数提取增强图序列模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-11 04:00

研究人员推出了一种新颖的框架HOPPER，旨在增强线性化图序列模型（LGSMs）。与依赖固定图算子的先前LGSMs不同，HOPPER能够实现跳数序列的端到端学习，从而实现针对特定图结构、节点特征和下游任务量身定制的自适应传播机制。这种方法保留了置换等变性，并在ECHO-Synth和LRIM等基准测试中展示了最先进或具有竞争力的性能，尤其是在图表示学习中处理长距离依赖关系方面。 AI

影响通过在序列模型中实现自适应传播机制来增强图表示学习。

排序理由该集群包含一篇详细介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Isuru Herath, Arin Gopakumar, Sharan Sahu · 2026-08-11 04:00

HOPPER：可学习的跳跃提取用于线性化图序列模型

arXiv:2608.09031v1 Announce Type: new Abstract: Graph neural networks typically propagate information through repeated message-passing layers, coupling the distance over which information travels with the number of nonlinear transformations applied. This coupling can make deep ar…

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HOPPER：可学习的跳跃提取用于线性化图序列模型

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