Node influence-based label propagation algorithm for semi-supervised learning

Abstract

Graph-based semi-supervised learning (GSSL) has received more and more attention due to its efficiency and accuracy. Label propagation is a critical step in GSSL that propagates label information to unlabeled data through the structure of graph. However, the traditional label propagation algorithms treat all unlabeled samples as equivalent and blindly propagate label information to all neighbors without considering their reliabilities. In this case, some unreliable samples may mislead the process of label propagation, thus greatly reducing the accuracy of classification. In order to solve this problem, this paper proposes a novel label propagation algorithm called node influence-based label propagation (NILP). Based on the structure of graph, the NILP algorithm measures the influences of nodes by calculating their degrees and local densities. In the process of label propagation, the label information is preferentially transmitted to the influential neighbors to control the propagation sequence and prevent wrong propagation. Moreover, our algorithm improves the transition matrix by integrating label information and feature information. The experimental results on both synthetic and real-world benchmark datasets show that the proposed method is superior to some existing label propagation algorithms. Especially when the number of labeled samples is very small, the advantage of NILP algorithm is more obvious.