Semi-Supervised Classification With Graph Structure Similarity and Extended Label Propagation

Graph-based semi-supervised learning (GSSL) has attracted great attention over the past decade. However, there are still several open problems: (1) how to construct a graph that effectively represents the underlying structure of data and (2) how to incorporate label information of the labeled samples into a procedure of label propagation. Our solution mainly focuses on two aspects: (1) we propose a new graph construction technique by fusing local and global structural similarity (FLGSS). Based on an initial graph structure such as K-nearest neighbors (KNN), we utilize different types of link prediction algorithms to extract local and global graph structure information. These two types of structure information are fused into a graph structure that enhances the ability to represent the data correlation. (2) By incorporating the label correlation with feature similarity of samples, we propose an extended label propagation algorithm (ELP). Through experiments on three different types of datasets, it is shown that our method outperforms other widely used graph construction methods. The extended label inference algorithm achieves better classification results than some state-of-the-art methods. The proposed FLGSS method starts from KNN graph and two link prediction algorithms are performed to construct the graph. With the time complexity analysis, we theoretically deduce that the time complexity of FLGSS is not beyond that of KNN. Meanwhile, the time complexity of ELP remains the same as that of the traditional LP algorithm.