Survey on Heterogeneous Graph Representation Learning

被引：0
作者：
Li, Ya-Cong [1 ]
Liu, Hao-Bing [1 ]
Jiang, Ruo-Bing [1 ]
Liu, Cong [2 ]
Zhu, Yan-Min [3 ]
机构：
[1] School of Computer Science and Technology, Ocean University of China, Qingdao
[2] School of Computer Science and Technology, Shandong University of Technology, Zibo
[3] Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai
来源：
Ruan Jian Xue Bao/Journal of Software | 2025年 / 36卷 / 06期
关键词：
data mining; deep learning; graph neural network (GNN); graph representation learning; heterogeneous graph;
D O I：
10.13328/j.cnki.jos.007319
中图分类号：
学科分类号：
摘要：
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. © 2025 Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：2794 / 2826
页数：32
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