Author Disambiguation through Adversarial Network Representation Learning

Abstract

Many persons share with the same name. Distinguishing different persons with the same name is important but challenging. Albeit much work has been proposed for author disambiguation, most of them do not adequately consider the heterogeneous relationships among authors and papers. In our work, ambiguous names and their related information, such as papers, conferences, titles, abstracts, etc., are constructed into a heterogeneous network which consists of different edge types. To fully incorporate all the information of the constructed network, we use Generative Adversarial Networks (GAN) to learn the network representation of the heterogeneous network. Although GAN has been used in many fields such as image generation, it hasn’t been used to obtain representations for the heterogeneous network. As far as we know, our work is the first work which use adversarial training to learn heterogeneous network representation. After the representations are learned, they are partitioned into different groups each representing distinct authors. After extensive experiments on three major author disambiguation datasets, we demonstrate that our method outperforms several state-of-the-art baselines in author disambiguation problem.