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English(EN) Chimaera: A Mixture-of-Graph-Experts Architecture for Cross-Task and Cross-Dataset Graph Learning

Chimaera架构通过专家混合增强图学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的图专家混合（Mixture-of-Graph-Experts）架构Chimaera，旨在增强跨各种任务和数据集的图学习能力。该架构集成了不同的图基础模型，包括图提示（graph prompts）和线性GNN模型，并利用大型语言模型进行嵌入生成。Chimaera扩展了现有的线性GNN，使其能够处理节点、链接和图级别的任务，并在基准数据集的实证分析中展现出强大的迁移能力和有效性。 AI

影响 引入了一种新的图学习架构，有望提高各种基于图的人工智能任务的性能。

排序理由 该集群描述了一篇介绍新图学习架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Chimaera架构通过专家混合增强图学习

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该集群描述了一篇介绍新图学习架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jonathan Frank, David Richerby, Ansgar Scherp ·

    Chimaera：一种用于跨任务和跨数据集图学习的混合图专家架构

    arXiv:2609.08709v1 Announce Type: new Abstract: Designing foundation models for graphs is challenging due to the irregular structure of graphs and the different sizes and characteristics of embeddings. Chimaera integrates mixture-of-experts with graph foundation models (GFM). It …

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