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基于GNN的链接预测调查：技术、应用与挑战A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges
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以下内容为 AI 自动翻译，英文原文附后供参考。
摘要
最近在arXiv上发布的一项调查深入探讨了图神经网络（GNN）在链接预测任务中的应用。这篇全面的综述对GNN技术和应用的进展进行了分类，阐明了各种架构及其各自的优缺点。论文还强调了GNN在知识图谱和推荐系统等领域的实际应用，为研究人员和从业者提供了宝贵的见解。
A recent survey published on arXiv delves into the realm of Graph Neural Networks (GNNs) specifically for link prediction tasks. This comprehensive review categorizes advancements in GNN techniques and applications, shedding light on various architectures and their respective strengths and limitations. The paper also emphasizes the practical applications of GNNs in areas such as knowledge graphs and recommendation systems, providing valuable insights for researchers and practitioners alike.
要点
调查根据技术和应用对GNN的进展进行了分类。
The survey categorizes GNN advancements based on techniques and applications.
强调了各种GNN架构的优缺点。
It highlights the strengths and limitations of various GNN architectures.
讨论了在知识图谱和推荐系统中的实际应用。
Real-world applications in knowledge graphs and recommendation systems are discussed.
为何重要
这项调查的重要性在于它整合了关于基于GNN的链接预测的现有知识，为未来的研究和应用开发提供指导。它填补了文献中的现有空白，为更有效的GNN实施铺平了道路。
This survey is significant as it consolidates existing knowledge on GNN-based link prediction, guiding future research and application development in this area. It addresses existing gaps in literature, paving the way for more effective GNN implementations.
产品经理
对于产品经理来说，了解GNN的进展可以为产品开发策略提供信息，特别是在链接预测发挥关键作用的推荐系统领域。
For product managers, understanding the advancements in GNNs can inform product development strategies, especially in areas like recommendation systems where link prediction plays a crucial role.
开发者
开发者和工程师可以利用这项调查中的见解来增强对GNN架构的理解，使他们能够构建更强大和有效的链接预测模型。
Developers and engineers can leverage the insights from this survey to enhance their understanding of GNN architectures, enabling them to build more robust and effective models for link prediction tasks.
学生与求职者
对于学生和求职者来说，这项调查是理解GNN研究当前格局的宝贵资源，使他们掌握在人工智能和机器学习领域高度相关的知识。
For students and job-seekers, this survey serves as a valuable resource to understand the current landscape of GNN research, equipping them with knowledge that is highly relevant in the job market focused on AI and machine learning.
覆盖来源
来源标题与摘录保留原文，以便核对与追溯。
arXiv cs.AI
A Survey on GNN-based Link Prediction: Techniques, Applications, and ChallengesThe paper provides a comprehensive review of Graph Neural Networks (GNNs) for link prediction, addressing gaps in existing literature by categorizing advancements based on techniques and applications. It discusses various GNN architectures and highlights their strengths, limitations, and real-world applications in knowledge graphs and recommendation systems.