AICurious Logo

What is: Deep Graph Convolutional Neural Network?

SourceAn End-to-End Deep Learning Architecture for Graph Classification
Year2000
Data SourceCC BY-SA - https://paperswithcode.com

DGCNN involves neural networks that read the graphs directly and learn a classification function. There are two main challenges: 1) how to extract useful features characterizing the rich information encoded in a graph for classification purpose, and 2) how to sequentially read a graph in a meaningful and consistent order. To address the first challenge, we design a localized graph convolution model and show its connection with two graph kernels. To address the second challenge, we design a novel SortPooling layer which sorts graph vertices in a consistent order so that traditional neural networks can be trained on the graphs.

Description and image from: An End-to-End Deep Learning Architecture for Graph Classification