Heterogeneous Graph Neural Network with Multi-view Representation Learning

TL;DR

This paper introduces MV-HetGNN, a novel heterogeneous graph neural network that leverages multi-view representation learning to better capture complex structural and semantic information, outperforming existing methods in various tasks.

Contribution

The paper proposes MV-HetGNN, a multi-view learning framework that integrates diverse semantic views for improved heterogeneous graph embedding.

Findings

MV-HetGNN outperforms state-of-the-art GNNs in node classification.

The model achieves superior results in node clustering.

It also enhances link prediction accuracy.

Abstract

Graph neural networks for heterogeneous graph embedding is to project nodes into a low-dimensional space by exploring the heterogeneity and semantics of the heterogeneous graph. However, on the one hand, most of existing heterogeneous graph embedding methods either insufficiently model the local structure under specific semantic, or neglect the heterogeneity when aggregating information from it. On the other hand, representations from multiple semantics are not comprehensively integrated to obtain versatile node embeddings. To address the problem, we propose a Heterogeneous Graph Neural Network with Multi-View Representation Learning (named MV-HetGNN) for heterogeneous graph embedding by introducing the idea of multi-view representation learning. The proposed model consists of node feature transformation, view-specific ego graph encoding and auto multi-view fusion to thoroughly learn complex structural and semantic information for generating comprehensive node representations. Extensive experiments on three real-world heterogeneous graph datasets show that the proposed MV-HetGNN model consistently outperforms all the state-of-the-art GNN baselines in various downstream tasks, e.g., node classification, node clustering, and link prediction.