# A Comprehensive Survey on Graph Neural Networks

## TL;DR

This survey provides a comprehensive overview of graph neural networks, categorizing recent models, discussing applications, and outlining future research directions in the field of non-Euclidean data analysis.

## Contribution

It introduces a new taxonomy for GNNs, summarizes applications and benchmarks, and discusses open research challenges in the rapidly evolving field.

## Key findings

- Proposes a taxonomy dividing GNNs into four categories.
- Summarizes applications across various domains.
- Highlights open research challenges and future directions.

## 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. The data in these tasks are typically represented in the Euclidean space. 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 complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. 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. 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. Finally, we propose potential research directions in this rapidly growing field.