GAF-Net: Graph attention fusion network for multi-view semi-supervised classification

Abstract

Multi-view semi-supervised classification is a typical task to classify data using a small amount of supervised information, which has attracted a lot of attention from researchers in recent years. In practice, existing methods tend to focus on extracting spatial or spectral features using graph neural networks without considering the diversity and variability of graph structures and the contributions of different views. To address this challenge, a framework termed graph attention fusion network is proposed, which consists of two phases: view-specific feature embedding and graph embedding fusion. In the former feature extraction stage, the view-specific feature embedding module can flexibly focus on the neighborhood calculation operation to learn a weight for each neighboring node. In the latter feature fusion stage, the graph embedding fusion module is performed by complementarity and consistency to fuse these embeddings for semi-supervised classification tasks. We carry out comprehensive experiments in semi-supervised classification on real-world datasets to substantiate the effectiveness of the proposed approach compared to several existing state-of-the-art methods.