A Semi-supervised Classification Approach for Multiple Time-Varying Networks with Total Variation

Abstract

In recent years, we have seen a surge of research on semi-supervised learning for improving classification performance due to the extreme imbalance between labeled and unlabeled data. In this paper, we innovatively propose a semi-supervised classification model for multiple time-varying networks, i.e., Multiple time-varying Networks Classification with Total variation (MNCT), which can integrate the multiple time-varying networks and select relevant ones. From a numerical point of view, the optimization is decomposed into two sub-problems, which can be solved efficiently under the alternating direction method of multipliers (ADMM) framework. Experimental results on both synthetic and real-world datasets empirically demonstrate the advantages of MNCT over state-of-the-art methods.