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English(EN) A Weisfeiler-Leman Characterization of Global-Attention Graph Transformers for Mixed-Integer Linear Programs

研究发现：图变换器在MILP上的表现受限于1-WL测试

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新论文对用于混合整数线性规划（MILP）的全局注意力图变换器的表达能力进行了特征化。研究证明，包括Graphormer和Set Transformer等架构在内的这些模型受到一维Weisfeiler-Leman（1-WL）测试的限制。这意味着在1-WL测试下等价的MILP实例将从这些变换器接收相同的图嵌入，从而无法恢复某些图不变量。研究表明，超越1-WL的表达能力主要来自输入编码，而非注意力机制本身。 AI

影响 特征化了图变换器的表达极限，可能指导未来复杂结构化数据模型的开发。

排序理由 学术论文发表在arXiv上，详细介绍了图变换器的理论局限性。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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研究发现：图变换器在MILP上的表现受限于1-WL测试

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学术论文发表在arXiv上，详细介绍了图变换器的理论局限性。[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) · Md Abrar Jahin, Craig A. Knoblock, Jay Pujara ·

    混合整数线性规划的全局注意力图变换器的Weisfeiler-Leman特征化

    arXiv:2607.17570v1 Announce Type: new Abstract: Graph foundation models (GFMs) with global attention are increasingly used to represent mixed-integer linear programs (MILPs), aiming to capture structure beyond the locality of standard graph neural networks. We study their express…

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