From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
This work addresses a theoretical gap in graph-based learning for researchers, proposing incremental improvements to enhance model integration and performance.
The paper tackles the relationship between traditional graph-based semi-supervised learning and graph convolutional networks, finding that typical GCNs may not integrate graph structure and label information effectively, and proposes three new graph convolution methods (OGC, GGC, GGCM) that show improved performance in experiments.
Graph-based semi-supervised learning (GSSL) has long been a hot research topic. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional networks (GCNs) have become the predominant techniques for their promising performance. In this paper, we theoretically discuss the relationship between these two types of methods in a unified optimization framework. One of the most intriguing findings is that, unlike traditional ones, typical GCNs may not jointly consider the graph structure and label information at each layer. Motivated by this, we further propose three simple but powerful graph convolution methods. The first is a supervised method OGC which guides the graph convolution process with labels. The others are two unsupervised methods: GGC and its multi-scale version GGCM, both aiming to preserve the graph structure information during the convolution process. Finally, we conduct extensive experiments to show the effectiveness of our methods. Code is available at https://github.com/zhengwang100/ogc_ggcm.