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Dec, 2023
学习重新加权用于图神经网络
Learning to Reweight for Graph Neural Network
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Zhengyu Chen, Teng Xiao, Kun Kuang, Zheqi Lv, Min Zhang...
TL;DR
本研究探讨了图神经网络在分布发生偏移的情况下的泛化能力问题,并提出了学习重加权以增强泛化能力的新方法L2R-GNN,通过对图表示变量进行聚类和学习权重以去除不同类别之间的相关性,有效改善了图神经网络的泛化能力,并在各种图预测基准测试中取得了优异的性能。
Abstract
graph neural networks
(GNNs) show promising results for graph tasks. However, existing GNNs'
generalization ability
will degrade when there exist distribution shifts between testing and training graph data. The c
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