A survey of graph neural networks and their industrial applications

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neurocomputing Pub Date : 2024-10-28 DOI:10.1016/j.neucom.2024.128761
Haoran Lu , Lei Wang , Xiaoliang Ma , Jun Cheng , Mengchu Zhou
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Abstract

Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and modeling graph-structured data. In recent years, GNNs have gained significant attention in various domains. This review paper aims to provide an overview of the state-of-the-art graph neural network techniques and their industrial applications. First, we introduce the fundamental concepts and architectures of GNNs, highlighting their ability to capture complex relationships and dependencies in graph data. We then delve into the variants and evolution of graphs, including directed graphs, heterogeneous graphs, dynamic graphs, and hypergraphs. Next, we discuss the interpretability of GNN, and GNN theory including graph augmentation, expressivity, and over-smoothing. Finally, we introduce the specific use cases of GNNs in industrial settings, including finance, biology, knowledge graphs, recommendation systems, Internet of Things (IoT), and knowledge distillation. This review paper highlights the immense potential of GNNs in solving real-world problems, while also addressing the challenges and opportunities for further advancement in this field.
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图神经网络及其工业应用概览
图神经网络(GNN)已成为分析和模拟图结构数据的强大工具。近年来,图神经网络在各个领域都获得了极大的关注。本综述旨在概述最先进的图神经网络技术及其工业应用。首先,我们将介绍图神经网络的基本概念和架构,强调其捕捉图数据中复杂关系和依赖性的能力。然后,我们深入探讨图的变体和演变,包括有向图、异构图、动态图和超图。接下来,我们将讨论 GNN 的可解释性以及 GNN 理论,包括图增强、表现力和过度平滑。最后,我们介绍了 GNN 在工业环境中的具体用例,包括金融、生物、知识图谱、推荐系统、物联网 (IoT) 和知识提炼。本综述论文强调了 GNN 在解决现实世界问题方面的巨大潜力,同时也探讨了该领域进一步发展所面临的挑战和机遇。
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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