Finally, we propose potential research directions in this rapidly growing field. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. Recently, many studies on extending deep learning approaches for graph data have emerged. And I obtained my Bachelor’s degree from Tongji University and University of Bologna. Before that, I obtained my Master’s degree from ETH Zürich and closely worked with Prof.

The complexity of graph data has imposed significant challenges on existing machine learning algorithms. student at the Computer Vision and Learning Group, ETH Zürich, supervised by Prof. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects.

The data in these tasks are typically represented in the Euclidean space. View a PDF of the paper titled A Comprehensive Survey on Graph Neural Networks, by Zonghan Wu and 5 other authors View PDF Abstract:Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding.

Yu pan and yan wu