English(EN) Heterogeneous Graph Condensation via Role-Aware Clustering

新的HGC-RC框架简化了异构图神经网络的训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-07 04:00

研究人员推出了一种新颖的框架HGC-RC，旨在提高在大型数据集上训练异构图神经网络（HGNNs）的效率。现有的图凝结方法通常不适用于异构图，依赖于计算密集型技术。HGC-RC通过轻量级传播生成增强的节点嵌入来解决这个问题。然后，它采用一种混合聚类策略，按类别划分标记节点，并按类型聚类未标记节点以维持关键连接。这种方法允许重建一个紧凑的异构图，在不影响性能的情况下显著加速HGNN训练。 AI

影响该框架为在大型异构图上加速HGNN训练提供了一个实际的解决方案，可能使这些模型的应用更加广泛。

排序理由该集群包含一篇研究论文，详细介绍了一种提高异构图神经网络效率的新方法。[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) · Fuyan Ou, Yulin Hu, Ye Yuan · 2026-07-07 04:00

通过角色感知聚类实现异构图压缩

arXiv:2607.03097v1 Announce Type: new Abstract: Heterogeneous Graph Neural Networks (HGNNs) have exhibited remarkable efficacy in modeling complex systems with multiple types of nodes and relations, yet their training on large-scale heterogeneous graphs remains computationally pr…

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通过角色感知聚类实现异构图压缩

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