Restage: Relation Structure-Aware Hierarchical Heterogeneous Graph Embedding

IF 6.6 1区 计算机科学 Q1 Multidisciplinary
Huanjing Zhao;Pinde Rui;Jie Chen;Shu Zhao;Yanping Zhang
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引用次数: 0

Abstract

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.
Restage:关系结构感知的分层异构图嵌入
异构图包含多种类型的实体和关系,能够模拟复杂的交互。异构图上的嵌入已成为分析和理解这类图的重要工具。虽然这些精心设计的方法取得了进展,但受限于模型设计和计算资源,难以扩展到大规模异构图数据,阻碍了这些方法的应用和推广。本文提出了关系结构感知的分层异构图嵌入框架 Restage。在这个框架下,只需用现有的图表示学习方法嵌入一个较小尺度的图,就足以获得原始异构图上的节点表示。我们考虑了异构图中的两种关系结构:交互关系和隶属关系。首先,我们设计了一种关系结构感知粗化方法,将原始图连续粗化到顶层,从而得到更小尺度的图。其次,我们允许任何无监督表示学习方法在顶层图上获取节点嵌入。最后,我们设计了一种关系结构感知细化方法,将节点嵌入从顶层图依次细化回原始图,从而获得原始图上的节点嵌入。在三个公共异构图数据集上的实验结果表明,所提出的 Restage 增强了表示学习方法的可扩展性。在另一个大规模图上,现有表示学习方法的速度最多提高了 18 倍。
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来源期刊
Tsinghua Science and Technology
Tsinghua Science and Technology COMPUTER SCIENCE, INFORMATION SYSTEMSCOMPU-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
10.20
自引率
10.60%
发文量
2340
期刊介绍: Tsinghua Science and Technology (Tsinghua Sci Technol) started publication in 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, electronic engineering, and other IT fields. Contributions all over the world are welcome.
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