图数据管理与图机器学习：协同效应与机遇
Graph Data Management and Graph Machine Learning: Synergies and Opportunities
AI总结：
本文综述了图数据管理与图机器学习的协同作用，详细阐述了图数据管理如何通过数据清洗和高效向量管理提升图机器学习性能，以及图机器学习如何辅助知识图谱查询等数据管理任务，并提出了未来的研究方向。
摘要：
机器学习，特别是深度学习，在图上的应用的普遍性在从化学信息学（药物发现）和生物信息学（蛋白质相互作用预测）到基于知识图谱的查询回答、欺诈检测和社交网络分析的应用中显而易见。同时，图数据管理致力于研发有效、高效、可扩展、稳健且用户友好的系统和算法，用于存储、处理和分析大量异构且复杂的图数据。我们的综述全面概述了图数据管理与图机器学习之间的协同作用，阐明了它们在整个图数据科学和机器学习流程中如何交织并相互促进。具体而言，本综述强调了两个关键方面：（1）图数据管理如何增强图机器学习，包括通过图数据清洗提升图神经网络性能、可扩展的图嵌入、高效的基于图的向量数据管理、稳健的图神经网络、用户友好的可解释性方法等贡献；以及（2）图机器学习反过来如何辅助图数据管理，重点关注知识图谱上的查询回答和各类数据科学任务等应用。我们讨论了相关的开放问题，并勾勒了关键的研究方向。
英文摘要：
The ubiquity of machine learning, particularly deep learning, applied to graphs is evident in applications ranging from cheminformatics (drug discovery) and bioinformatics (protein interaction prediction) to knowledge graph-based query answering, fraud detection, and social network analysis. Concurrently, graph data management deals with the research and development of effective, efficient, scalable, robust, and user-friendly systems and algorithms for storing, processing, and analyzing vast quantities of heterogeneous and complex graph data. Our survey provides a comprehensive overview of the synergies between graph data management and graph machine learning, illustrating how they intertwine and mutually reinforce each other across the entire spectrum of the graph data science and machine learning pipeline. Specifically, the survey highlights two crucial aspects: (1) How graph data management enhances graph machine learning, including contributions such as improved graph neural network performance through graph data cleaning, scalable graph embedding, efficient graph-based vector data management, robust graph neural networks, user-friendly explainability methods; and (2) how graph machine learning, in turn, aids in graph data management, with a focus on applications like query answering over knowledge graphs and various data science tasks. We discuss pertinent open problems and delineate crucial research directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
