Hierarchical Semi-Supervised Representation Learning for Cyber Physical Social Intelligence

In the context of Cyber Physical Social Intelligence (CPSI), efficiently training and inferring from samples with limited labels poses critical challenges due to the scarcity and high cost of label acquisition for big data. The aim is to attain high accuracy at minimal cost, thereby enhancing adaptation to the CPSI scenario. To tackle the challenges in CPSI, we present a multi-level feature learning framework for semi-supervised classification tasks. Initially, we employ a mapping operation for each view, extracting view-specific features with a feature-level reconstruction loss. These features are fused to obtain a shared feature. Simultaneously, a learnable graph neural network captures global topology using a graph structure-level reconstruction loss. Subsequently, a scalable graph convolution fusion module combines these features. Our evaluations on eight benchmark datasets show promising results and empirically prove the effectiveness of our approach, surpassing eight state-of-the-art methods in multi-view semi-supervised classification tasks.