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English(EN) Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning

新的AGREE框架统一了用于图聚类的异构属性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的框架AGREE，旨在应对异构属性图聚类的挑战。该端到端系统通过多级对齐和基于相似性的构建，将数值和类别数据等多种属性类型与图拓扑统一起来。AGREE采用基于四元数的图卷积来增强属性交互并减轻表示退化，同时利用浅层图架构来解决过平滑效应。该框架联合优化图重构和聚类的嵌入，在准确性、鲁棒性和适应性方面在各种基准测试中均表现出色。 AI

影响 这项研究可以提高具有复杂异构数据的领域中基于图的机器学习模型的准确性和适应性。

排序理由 该项目是一篇研究论文，详细介绍了一种用于图聚类的新框架和方法。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的AGREE框架统一了用于图聚类的异构属性

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该项目是一篇研究论文，详细介绍了一种用于图聚类的新框架和方法。 [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) · Xiang Zhang ·

    弥合鸿沟：通过四元数表示学习实现异构属性图聚类

    Attributed graph clustering partitions nodes by jointly exploiting node attributes and graph topology. It remains challenging due to attribute heterogeneity and representation degradation during graph learning. Real-world datasets often contain heterogeneous attributes, i.e., num…

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