Multiplex Heterogeneous Graph Convolutional Network

Abstract

Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed nodes and 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 heterogeneous network embedding. Our MHGCN can automatically learn the useful heterogeneous meta-path interactions of different lengths 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 five 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.

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Cited by in corpus (7)

• Multi-Behavior Graph Neural Networks for Recommender System
• HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning
• Homophily-oriented Heterogeneous Graph Rewiring
• Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation
• Convolutional Learning on Multigraphs
• Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning
• UMGAD: Unsupervised Multiplex Graph Anomaly Detection