论文标题
将半监督图卷积网络正规化，并具有多种光滑度损失
Regularizing Semi-supervised Graph Convolutional Networks with a Manifold Smoothness Loss
论文作者
论文摘要
现有的图形卷积网络集中在邻里聚合方案上。当应用于半监督的学习中时，他们通常会遭受过度拟合问题的困扰，因为网络受到跨凝性损失的训练，这是一小片标记的数据。在本文中，我们提出了针对图形结构定义的无监督的平滑度损失，可以将其添加到损耗函数中作为正则化。我们通过迭代扩散过程在拟议的损失之间建立联系，并表明将损失最小化等于与无限层的汇总邻居预测。我们对多层感知器和现有图形网络进行实验，并证明添加提出的损失可以始终如一地改善性能。
Existing graph convolutional networks focus on the neighborhood aggregation scheme. When applied to semi-supervised learning, they often suffer from the overfitting problem as the networks are trained with the cross-entropy loss on a small potion of labeled data. In this paper, we propose an unsupervised manifold smoothness loss defined with respect to the graph structure, which can be added to the loss function as a regularization. We draw connections between the proposed loss with an iterative diffusion process, and show that minimizing the loss is equivalent to aggregate neighbor predictions with infinite layers. We conduct experiments on multi-layer perceptron and existing graph networks, and demonstrate that adding the proposed loss can improve the performance consistently.
