Multi-Relation Augmentation for Graph Neural Networks

Abstract

Data augmentation has been successfully utilized to refine the generalization capability and performance of learning algorithms in image and text analysis. With the rising focus on graph neural networks, an increasing number of researchers are employing various data augmentation approaches to improve graph learning techniques. Although significant improvements have been made, most of them are implemented by manipulating nodes or edges to generate modified graphs as augmented views, which might lose the information hidden in the input data. To address this issue, we propose a simple but effective data augmentation framework termed multi-relation augmentation designed for existing graph neural networks. Different from prior works, the designed model utilizes various methods to simulate multiple adjacency relationships (multi-relation) among nodes as augmented views instead of manipulating the original graph. The proposed augmentation framework can be formulated as three sub-modules, each offering distinct advantages: 1) The encoder module and projection module form a shared contrastive learning framework for both the original graph and all augmented views. Due to the shared mechanism, the proposed method can be simply applied to various graph learning models. 2) The designed task-specific module flexibly extends the proposed framework for various machine learning tasks. Experimental results on several databases show that the introduced augmentation framework improves the performance of existing graph neural networks on both semi-supervised node classification and unsupervised clustering tasks. It demonstrates that multiple relations mechanism is efficient for graph-based augmentation.