Restage:Relation Structure-Aware Hierarchical Heterogeneous Graph Embedding  

作　　者：Huanjing Zhao Pinde Rui Jie Chen Shu Zhao Yanping Zhang 

机构地区：[1]School of Computer Science and Technology,Anhui University,and also with Information Materials and Intelligent Sensing Laboratory of Anhui Province,Hefei 230601,China

出　　处：《Tsinghua Science and Technology》2025年第1期198-214,共17页清华大学学报自然科学版(英文版)

基　　金：supported by the National Natural Science Foundation of China(Nos.1876001,61602003,and 61673020);the National High Technology Research and Development Program(No.2017YFB1401903);the Provincial Natural Science Foundation of Anhui Province(No.1708085QF156).

摘　　要：Heterogeneous graphs contain multiple types of entities and relations,which are capable of modeling complex interactions.Embedding on heterogeneous graphs has become an essential tool for analyzing and understanding such graphs.Although these meticulously designed methods make progress,they are limited by model design and computational resources,making it difficult to scale to large-scale heterogeneous graph data and hindering the application and promotion of these methods.In this paper,we propose Restage,a relation structure-aware hierarchical heterogeneous graph embedding framework.Under this framework,embedding only a smaller-scale graph with existing graph representation learning methods is sufficient to obtain node representations on the original heterogeneous graph.We consider two types of relation structures in heterogeneous graphs:interaction relations and affiliation relations.Firstly,we design a relation structure-aware coarsening method to successively coarsen the original graph to the top-level layer,resulting in a smaller-scale graph.Secondly,we allow any unsupervised representation learning methods to obtain node embeddings on the top-level graph.Finally,we design a relation structure-aware refinement method to successively refine the node embeddings from the top-level graph back to the original graph,obtaining node embeddings on the original graph.Experimental results on three public heterogeneous graph datasets demonstrate the enhanced scalability of representation learning methods by the proposed Restage.On another large-scale graph,the speed of existing representation learning methods is increased by up to eighteen times at most.

关 键 词：heterogeneous graph graph embedding relation structure HIERARCHICAL 

分 类 号：O15[理学—数学]