English(EN) Embedded Graph Flows for Categorical Graph Generation

嵌入式图流模型推动分类图生成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员开发了嵌入式图流（EGF），一种新颖的生成模型，专为分类图生成而设计。与使用固定独热向量表示类别的先前方法不同，EGF学习节点和边类型的连续嵌入，通过置换等变图变换器传输高斯噪声。这种方法允许更灵活和准确地表示分类数据。EGF在分子基准测试中表现出有竞争力的性能，在QM9数据集上取得了最先进的结果，并在ZINC250k数据集上与参考分子显示出高度一致性。 AI

影响这一新模型可以提高生成复杂分类数据的准确性和灵活性，特别是在分子发现和药物开发领域。

排序理由该集群包含一篇详细介绍新模型和基准测试结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Ethan Ma, Zihan Wang, Chris Siu Yeung Chow, Xinguo Feng, Qingqing Li, Rui Jiang, Naipeng Dong, Guangdong Bai · 2026-09-07 04:00

用于分类图生成的嵌入式图流

arXiv:2609.05328v1 Announce Type: new Abstract: Generating categorical graphs requires choosing node and edge types that form a coherent structure without depending on node order. Many graph generators encode categories as fixed one-hot vectors, which can impose an artificial geo…

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用于分类图生成的嵌入式图流

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