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Jun, 2024
基于拓扑感知的图分布偏移动态重新加权
Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
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Weihuang Zheng, Jiashuo Liu, Jiaxing Li, Jiayun Wu, Peng Cui...
TL;DR
采用拓扑感知动态重新加权(TAR)框架,通过在几何Wasserstein空间中的梯度流动态调整样本权重,以提供分布鲁棒性,从而增强图数据的域外泛化性能。
Abstract
graph neural networks
(GNNs) are widely used for node classification tasks but often fail to generalize when training and test nodes come from different distributions, limiting their practicality. To overcome this, recent approaches adopt
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