A parameter-free label propagation algorithm using bipartite heterogeneous networks for text classification

Abstract

A bipartite heterogeneous network is one of the simplest ways to represent a textual document collection. In such case, the network consists of two types of vertices, representing documents and terms, and links connecting terms to the documents. Transductive algorithms are usually applied to perform classification of networked objects. This type of classification is usually applied when few labeled examples are available, which may be worthwhile for practical situations. Nevertheless, for existing transductive algorithms users have to set several parameters that significantly affect the classification accuracy. In this paper, we propose a parameter-free algorithm for transductive classification of textual data, referred to as LPBHN (Label Propagation using Bipartite Heterogeneous Networks). LPBHN uses a bipartite heterogeneous network to perform the classification task. The proposed algorithm presents accuracy equivalent or higher than state-of-the-art algorithms for transductive classification in heterogeneous or homogeneous networks.