English(EN) Inductive Correlation Clustering with Graph Neural Networks

图神经网络解决相关性聚类可扩展性问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员开发了一个新颖的框架，使用图神经网络（GNNs）来解决传统相关性聚类（CC）算法的可扩展性和转导性限制。这种新方法实现了归纳相关性聚类，通过在训练过程中学习通用的结构模式和节点特征，使模型能够泛化到未见过的图实例。该框架显著减少了推理时间，同时保持了具有竞争力的近似比，并且在图分类任务中也显示出作为可学习池化机制的潜力。 AI

影响这项研究可能显著提高聚类算法在大规模图数据分析中的效率和适用性。

排序理由该集群包含一篇学术论文，详细介绍了使用图神经网络进行相关性聚类的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Francesco Paolo Nerini, Francesco Bonchi, Arijit Khan, Andr\'e Panisson · 2026-08-28 04:00

基于图神经网络的归纳相关聚类

arXiv:2608.27153v1 Announce Type: new Abstract: Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and does not require a pre-specified number of clusters. Given $n$ objects and a pairwi…

本文如何被排名

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基于图神经网络的归纳相关聚类

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