Dual graph convolutional network
时间: 2023-09-30 17:08:10 浏览: 45
Dual Graph Convolutional Network (DGCN) is a type of neural network designed to work with graph data. It uses two types of graphs - the primal graph and the dual graph - to represent the input data. The primal graph represents the original data and the dual graph represents the relationships between the original data points.
DGCN works by performing convolution operations on both the primal and dual graphs. This allows the network to capture both local and global relationships between data points. The convolution operation on the primal graph captures local information while the convolution operation on the dual graph captures global information.
DGCN has been used in various applications such as image classification, social network analysis, and recommendation systems. It has shown promising results in these applications and is considered to be a powerful tool for working with graph data.