A Brief Review of Receptive Fields in Graph Convolutional Networks

Abstract

Convolutional neural networks have been shown successful in extracting features from images and texts. However, it is difficult to apply convolutional neural networks directly on ubiquitous graph data since the graph data lies in an irregular structure. A significant number of researchers engrossed themselves in studying graph convolutional networks transformed from Euclidean domain. Previous graph convolutional networks overviews mainly focus on reviewing recent methods in a comprehensive ways. In this survey, we review the convolutional networks from the perspective of receptive fields. Roughly, the convolutional networks fall into three main categories: spectral based methods, sampling based methods and attention based methods. We analysis the differences of these methods and propose three potential directions for future research of graph convolutional networks.