| 摘要: |
| 异质图因其能够反映现实世界中实体及其复杂多样的关系, 而在诸多领域中扮演着至关重要的角色. 异质图表征学习技术, 旨在将图中的信息有效地映射到低维空间中, 以便捕获和利用节点间深层的语义关联, 进而支持节点分类、聚类等下游分析任务. 深入调研异质图表征学习的最新研究进展, 涵盖方法论和应用实践. 首先对异质图的基本概念进行形式化定义, 并讨论异质图表征学习的挑战. 此外, 从浅层模型和深度模型两个角度, 系统地回顾当前主流的异质图表征学习方法, 特别是深度模型, 从异质图转换的视角出发进行分类并分析, 进而对多种方法的优势、局限和适用场景进行详尽分析, 旨在为读者提供一个全面的研究视角. 此外, 还介绍异质图表征学习研究中常用的数据集和工具, 并探讨其在现实世界中的典型应用. 最后, 总结主要贡献, 并对异质图表征学习领域的未来研究方向展望. 本综述旨在为研究者提供一个关于异质图表征学习领域的全面认识, 为未来的研究和应用奠定坚实的基础. |
| 关键词: 异质图 图表征学习 图神经网络 深度学习 数据挖掘 |
| DOI：10.13328/j.cnki.jos.007319 |
| 分类号: |
| 基金项目:国家自然科学基金 (62302469); 山东省自然科学基金 (ZR2023QF100, ZR2022QF050); 山东省泰山学者工程专项基金 (tsqn201909109); 山东省自然科学基金优秀青年基金 (ZR2021YQ45); 山东省高等学校青创科技计划创新团队项目(2021KJ031) |
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| Survey on Heterogeneous Graph Representation Learning |
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LI Ya-Cong1, LIU Hao-Bing1, JIANG Ruo-Bing1, LIU Cong2, ZHU Yan-Min3
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1.School of Computer Science and Technology, Ocean University of China, Qingdao 266003, China;2.School of Computer Science and Technology, Shandong University of Technology, Zibo 255049, China;3.Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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| Abstract: |
| Heterogeneous graphs, which can effectively capture the complex and diverse relationships between entities in the real world, play a crucial role in many domains. Heterogeneous graph representation learning aims to map the information in graphs into a low-dimensional space, so as to capture the deep semantic associations between nodes and support downstream tasks such as node classification and clustering. This study presents a comprehensive review of the latest research progress in heterogeneous graph representation learning, covering both methodological advancements and real-world applications. It first formally defines the concept of heterogeneous graphs and discusses the key challenges in heterogeneous graph representation learning. From the perspectives of shallow models and deep models. It then systematically reviews the mainstream methods for heterogeneous graph representation learning, with a particular focus on deep models. Especially for deep models, they are categorized and analyzed from the perspective of heterogeneous graph transformation. The strengths, limitations, and application scenarios of various methods are thoroughly analyzed, aiming to provide readers with a holistic research perspective. Furthermore, the commonly used datasets and tools in the field of heterogeneous graph representation learning are introduced, and their applications in the real world are discussed. Finally, the main contributions of this study are summarized and the outlook on the future research directions in this area is presented. This study intends to offer researchers a comprehensive understanding of the field of heterogeneous graph representation learning, laying a solid foundation for future research and application. |
| Key words: heterogeneous graph graph representation learning graph neural network (GNN) deep learning data mining |
