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Updated: May 24, 2025

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超越消息传递:通过特征扰动对图形神经网络进行概括,用于半监督节点分类

Yoonhyuk Choi, Jiho Choi, Taewook Ko

IEEE transactions on neural networks and learning systems

|March 3, 2025

概括

这项研究引入了一种新的扰动技术,用于对抗由稀疏节点特征引起的图形神经网络 (GNN) 过拟合. 该方法通过提高训练可变性和减少预测差异来提高节点分类性能.

科学领域:

机器学习机器学习
图形神经网络图形神经网络

背景情况:

图形神经网络 (GNN) 在半监督学习中被广泛使用,研究重点是有效的图形过器和聚合方法.
由于训练节点和特征稀疏 (例如,词袋),导致投影矩阵过度拟合,因此存在挑战.

研究的目的:

为了解决GNN中因稀疏节点特征引起的过度配合问题.
提出一种创新的扰动技术,以提高GNN的性能.

主要方法:

引入一种新的扰动技术,修改初始特征和超平面.
增加训练可变性以更新所有维度并减少预测差异.

主要成果:

拟议的方法显著提高了对现实世界数据集的节点分类性能.
在GNN算法中实现了高达46.5%的改进.

结论:

这种方法是第一个解决由稀疏节点特征引起的GNN过拟合的方法.
扰动技术有效地减轻了过拟合,并提高了分类准确性.

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