Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks

27 Feb 2020Zhiqian ChenFanglan ChenLei ZhangTaoran JiKaiqun FuLiang ZhaoFeng ChenChang-Tien Lu

The success of deep learning has been widely recognized in many machine learning tasks during the last decades, ranging from image classification and speech recognition to natural language understanding. As an extension of deep learning, Graph neural networks (GNNs) are designed to solve the non-Euclidean problems on graph-structured data which can hardly be handled by general deep learning techniques... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet