Semi-supervised multi-view graph convolutional networks with application to webpage classification

Semi-supervised multi-view learning (SML) is a hot research topic in recent years, with webpage classification being a typical application domain. The performance of SML is fur-ther boosted by the successful introduction of graph convolutional network (GCN) for learning discriminant node representations. However, there remains much space to improve the GCN-based SML technique, particularly on how to adaptively learn optimal graph structures for multi-view graph convolutional representation learning and make full use of the label and structure information in labeled and unlabeled multi-view samples. In this paper, we propose a novel SML approach named semi-supervised multi-view graph convolutional networks (SMGCN) for webpage classification. It contains a multi-view graph construction module and a semi-supervised multi-view graph convolutional repre-sentation learning module, which are integrated into a unified network architecture. The former aims to obtain optimal graph structure for each view. And the latter performs graph convolutional representation learning for each view, and provides an inter-view attention scheme to fuse multi-view representations. Network training is guided by the losses defined on both label and feature spaces, such that the label and structure information in labeled and unlabeled data is fully explored. Experiments on two widely used webpage datasets demonstrate that SMGCN can achieve state-of-the-art classification performance. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

Semi-supervised multi-view learning (SML) is a hot research topic that has gained attention in recent years, particularly in the domain of webpage classification. This paper proposes a novel approach called semi-supervised multi-view graph convolutional networks (SMGCN) for improving the performance of SML. The approach learns optimal graph structures and fuses multi-view representations to achieve state-of-the-art classification performance in webpage classification.