论文标题
在正规化理论的背景下设计光谱图卷积神经网络过滤器的框架
Framework for Designing Filters of Spectral Graph Convolutional Neural Networks in the Context of Regularization Theory
论文作者
论文摘要
图卷积神经网络（GCNN）已被广泛用于图学习。已经观察到可以根据图拉普拉斯式定义图上的平滑度。这个事实指出的是使用Laplacian在图形上得出正则化操作员及其在光谱GCNN滤波器设计上的使用。在这项工作中，我们探索了图拉普拉斯式的正则化属性，并为光谱GCNN中的正则滤波器设计提出了广义框架。我们发现，许多最先进的GCNN中使用的过滤器可以作为我们开发的框架的特殊情况。我们设计了与定义明确的正规化行为相关的新过滤器，并在半监督节点分类任务上测试了其性能。发现它们的性能优于其他最先进的技术。
Graph convolutional neural networks (GCNNs) have been widely used in graph learning. It has been observed that the smoothness functional on graphs can be defined in terms of the graph Laplacian. This fact points out in the direction of using Laplacian in deriving regularization operators on graphs and its consequent use with spectral GCNN filter designs. In this work, we explore the regularization properties of graph Laplacian and proposed a generalized framework for regularized filter designs in spectral GCNNs. We found that the filters used in many state-of-the-art GCNNs can be derived as a special case of the framework we developed. We designed new filters that are associated with well-defined regularization behavior and tested their performance on semi-supervised node classification tasks. Their performance was found to be superior to that of the other state-of-the-art techniques.
