新的TPAGP方法通过保持拓扑结构来增强图表示

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

研究人员引入了一种名为拓扑保持自适应图池化（TPAGP）的新方法，旨在通过捕获全局和局部拓扑结构来改进图表示。与之前通过移除或合并节点来逐步粗化图的现有方法不同，TPAGP使用节点特征和拓扑信息将图动态地划分为“颗粒球”。这种方法生成多粒度表示，促进不同细节层次之间的特征交互，从而提高图分类任务的性能。实验表明，TPAGP通过有效减少与固定粒度策略相关的信息丢失，优于现有的池化方法。 AI

影响通过改进复杂拓扑结构的表示来增强图分类性能。

排序理由该项目是一篇学术论文，详细介绍了一种新的图池化方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Sen Zhao, Gaojie Xu, Shuyin Xia, Yifan Guan, Yi Liu, Yi Wang, Wei Wang · 2026-09-07 04:00

全球到本地：通过粒状球实现拓扑保持的自适应图池化

arXiv:2609.04978v1 Announce Type: new Abstract: Graph pooling aims to compress the graph, including both node embeddings and their underlying topological patterns, into a more compact representation. Previous works focus primarily on the overly fine-grained representation of node…

本文如何被排名

报道来源 [1]

全球到本地：通过粒状球实现拓扑保持的自适应图池化

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