<p>Herein, we propose an automated essay scoring (AES) model that utilizes the argumentative graph (AG) information of an essay. An AG is a logical structure that influences the holistic score and key aspects of organization in scoring. Previous AES models have considered the statistics of logical elements of the graph and the relationships among these elements. In this study, we employ entire sentence containing logical elements to estimate the relationships among the sentences. Then, the proposed method extracts the relevant features using a graph attention network; this network considers the graph structure and is combined with a conventional AES model. We conducted experiments on the Automated Student Assessment Prize (ASAP) and demonstrated that the proposed model exhibits improved accuracy compared to similar AG-based baseline methods. Furthermore, we validated AG and demonstrated that the proposed method achieves high accuracy when AG is random or no logical structure exists in an essay.</p>