Deep random walk inspired multi-view graph convolutional networks for semi-supervised classification

Abstract

Recent studies highlight the growing appeal of multi-view learning due to its enhanced generalization. Semi-supervised classification, using few labeled samples to classify the unlabeled majority, is gaining popularity for its time and cost efficiency, particularly with high-dimensional and large-scale multi-view data. Existing graph-based methods for multi-view semi-supervised classification still have potential for improvement in further enhancing classification accuracy. Since deep random walk has demonstrated promising performance across diverse fields and shows potential for semi-supervised classification. This paper proposes a deep random walk inspired multi-view graph convolutional network model for semi-supervised classification tasks that builds signal propagation between connected vertices of the graph based on transfer probabilities. The learned representation matrices from different views are fused by an aggregator to learn appropriate weights, which are then normalized for label prediction. The proposed method partially reduces overfitting, and comprehensive experiments show it delivers impressive performance compared to other state-of-the-art algorithms, with classification accuracy improving by more than 5% on certain test datasets.