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English(EN) CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts

CHoE 方法通过专家网络增强跨域图学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 CHoE，一种用于异构图提示学习 (HGPL) 的新颖方法，旨在克服跨域应用的局限性。与主要在域内的现有方法不同，CHoE 利用了结构条件专家网络。通过采用结构感知路由机制来选择兼容的专家并整合多视图表示进行预测，这种方法能够在少样本跨域场景中实现更好的性能。实验表明，CHoE 在跨域少样本学习方面持续优于基线方法。 AI

影响 增强了跨不同域的异构图数据的少样本学习能力。

排序理由 该集群包含一篇详细介绍图学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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CHoE 方法通过专家网络增强跨域图学习

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该集群包含一篇详细介绍图学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Peiyuan Li, Yongqi Huang, Jitao Zhao, Dongxiao He, Di Jin, Weixiong Zhang ·

    CHoE：通过结构条件专家实现跨域异构图提示学习

    arXiv:2605.15888v2 Announce Type: replace-cross Abstract: Heterogeneous Graph Prompt Learning (HGPL)has emerged as a promising paradigm for bridging the gap between the objectives of pre-training foundation models and their downstream applications in heterogeneous graph settings.…

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