A Comprehensive Survey on Graph Neural Networks(图神经网络综合研究)

最新推荐文章于 2025-07-15 09:44:53 发布

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最新推荐文章于 2025-07-15 09:44:53 发布

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本文是一篇对图神经网络（GNN）的综合研究，介绍了GNN的历史、定义和分类，重点探讨了图卷积网络（GCN）、图注意力网络、图生成网络和图时空网络等架构。研究强调了图数据的复杂性及其对传统机器学习算法的挑战，并概述了GNN在推荐系统、药物发现、图像分析、社交网络分析等多个领域的应用。此外，还讨论了未来的研究方向和开源实现。

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A Comprehensive Survey on Graph Neural Networks

图神经网络综合研究

Zonghan Wu, Shirui Pan, Member, IEEE, Fengwen Chen, Guodong Long,

Chengqi Zhang, Senior Member, IEEE, Philip S. Yu, Fellow, IEEE

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 c

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