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CoRe-GNN 通过双重消息传递推动图神经网络训练

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种在大型图上训练图神经网络（GNN）的新方法 CoRe-GNN。该方法通过并行执行两种消息传递来解决内存限制：一种粗粒化的簇间项用于长程结构，一种局部的簇内项用于节点特定细节。CoRe-GNN 旨在结合图粗化和 Cluster-GCN 的优点，在各种节点分类基准测试中（包括具有长程依赖性的测试）优于这些基线方法，同时保持内存效率。 AI

影响 CoRe-GNN 为在大型数据集上训练图神经网络提供了一种更节省内存且更准确的方法，有望提高推荐系统和社交网络分析等应用的性能。

排序理由 该集群描述了一篇详细介绍图神经网络新方法的最新研究论文。

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CoRe-GNN 通过双重消息传递推动图神经网络训练

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Antonin Joly, Nicolas Keriven, Aline Roumy ·

    CoRe-GNN: 多层粗粒化图上的消息传递

    arXiv:2608.02128v1 Announce Type: new Abstract: Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the G…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    CoRe-GNN: 多层粗粒化图上的消息传递

    Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the GNN propagation matrix, providing a unified persp…

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