MHGCN+: Multiplex Heterogeneous Graph Convolutional Network

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chaofan Fu, Pengyang Yu, Yanwei Yu, Chao Huang, Zhongying Zhao, Junyu Dong
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引用次数: 0

Abstract

Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous graph data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex networks between multi-typed nodes and the different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN+) for multiplex heterogeneous network embedding. Our MHGCN+ can automatically learn the useful heterogeneous meta-path interactions of different lengths with different importance in multiplex heterogeneous networks through multi-layer convolution aggregation. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments on seven real-world datasets with various network analytical tasks demonstrate the significant superiority of MHGCN+ against state-of-the-art embedding baselines in terms of all evaluation metrics. The source code of our method is available at: https://github.com/FuChF/MHGCN-plus.

MHGCN+:多重异构图卷积网络
异构图卷积网络在处理异构图数据的各种网络分析任务中广受欢迎,从链接预测到节点分类,不一而足。然而,现有的大多数工作都忽略了多类型节点之间的多重网络关系异质性,以及节点嵌入的元路径中关系的不同重要性,从而难以捕捉不同关系之间的异构结构信号。为应对这一挑战,本研究提出了用于多重异构网络嵌入的多重异构图卷积网络(MHGCN+)。我们的 MHGCN+ 可以通过多层卷积聚合,自动学习多重异构网络中不同长度、不同重要性的有用异构元路径交互。此外,我们还通过无监督和半监督学习范式,将多关系结构信号和属性语义有效地整合到学习到的节点嵌入中。在七个真实世界数据集上进行的各种网络分析任务的广泛实验表明,在所有评估指标方面,MHGCN+ 都明显优于最先进的嵌入基线。我们方法的源代码可在以下网址获取:https://github.com/FuChF/MHGCN-plus。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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