English(EN) CoPE-CGL: Community-aware Prototype Extraction for Replay-Free Continual Graph Learning

新的CoPE-CGL框架推动了无重放持续图学习的发展

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员推出了一种新颖的持续图学习（Continual Graph Learning）框架CoPE-CGL，它在不重放过去数据或任务标识符的情况下解决了灾难性遗忘问题。该方法利用了从多跳拉普拉斯平滑派生的无参数结构先验，并结合了社区级原型和动态路由。CoPE-CGL在各种基准测试中表现强劲，性能可与现有的基于重放的方法相媲美或超越，同时运行时更快，存储需求更低。 AI

影响该框架提供了一种更有效的持续图学习方法，有可能使模型能够以更低的计算开销和存储需求适应不断变化的图数据。

排序理由这是一篇详细介绍持续图学习新方法的学术论文。[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) · Bo-Young Lim, Seokhwan Kim, Kisung Lee, Hyuk-Yoon Kwon · 2026-10-28 04:00

CoPE-CGL：社区感知原型提取用于无重放的持续图学习

arXiv:2610.35313v2 Announce Type: new Abstract: Continual graph learning (CGL) maintains models as graphs evolve without retraining from scratch, while mitigating catastrophic forgetting. Existing methods typically target either transductive or inductive settings and often store …

本文如何被排名

报道来源 [1]

CoPE-CGL：社区感知原型提取用于无重放的持续图学习

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