通过关系嵌入实现同构GNN处理异构图

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Big Data Pub Date : 2023-09-08 DOI:10.1109/TBDATA.2023.3313031
Junfu Wang;Yuanfang Guo;Liang Yang;Yunhong Wang
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引用次数: 0

摘要

图神经网络(gnn)已被各种方法推广到处理异构图。不幸的是,这些方法通常通过各种复杂的模块对异构性进行建模。本文旨在提出一个简单而有效的框架,赋予同构gnn足够的能力来处理异构图。具体来说,我们提出了基于关系嵌入的图神经网络(RE-GNN),它只使用每个关系的一个参数来嵌入不同类型的关系和节点类型特定的自环连接的重要性。为了同时优化这些关系嵌入和模型参数,提出了一个梯度缩放因子来约束嵌入收敛到合适的值。此外,我们从两个角度对我们提出的RE-GNN进行了解释,并从理论上证明了我们的RE-GCN比GTN(典型的异构GNN,可以自适应生成元路径)具有更强的表达能力。大量的实验表明,我们的RE-GNN可以有效地处理异构图,并且可以应用于各种同质gnn。
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Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding
Graph Neural Networks (GNNs) have been generalized to process the heterogeneous graphs by various approaches. Unfortunately, these approaches usually model the heterogeneity via various complicated modules. This article aims to propose a simple yet effective framework to assign adequate ability to the homogeneous GNNs to handle the heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Network (RE-GNN), which employs only one parameter per relation to embed the importance of distinct types of relations and node-type-specific self-loop connections. To optimize these relation embeddings and the model parameters simultaneously, a gradient scaling factor is proposed to constrain the embeddings to converge to suitable values. Besides, we interpret the proposed RE-GNN from two perspectives, and theoretically demonstrate that our RE-GCN possesses more expressive power than GTN (which is a typical heterogeneous GNN, and it can generate meta-paths adaptively). Extensive experiments demonstrate that our RE-GNN can effectively and efficiently handle the heterogeneous graphs and can be applied to various homogeneous GNNs.
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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