关键词: 异构网络, 图卷积网络, 网络表示学习

Abstract:

Many scenarios in the real world can be represented by heterogeneous networks composed of multiple node types and edge types. The networks are rich in semantic relationships and have practical applications that have attracted the attention of academic and industry. Traditional approaches are based on shallow models for heterogeneous network mining. In recent years, due to the superior performance of graph convolutional networks in homogeneous networks, many scholars have applied graph convolutional networks to mining heterogeneous networks and achieved excellent results in various tasks. The research progress of heterogeneous graph convolutional networks is reviewed to understand the development of related fields. The development of heterogeneous graph convolutional networks is introduced, and the heterogeneous graph convolutional networks are divided into models based on meta-path and adaptive heterogeneous information are introduced and summarized in detail, and different aggregation methods are synthesized and analyzed. The applications of heterogeneous graph convolutional networks in recommendation systems, biochemistry, anomaly detection and natural language processing are introduced. The future challenges of heterogeneous graph convolutional networks and the problems worthy of research are analyzed.

Key words: heterogeneous networks, graph convolutional networks, network representation learning