Structural Data Recognition With Graph Model Boosting

Abstract

This paper presents a novel method for structural data recognition using a large number of graph models. In general, prevalent methods for structural data recognition have two shortcomings: 1) Only a single model is used to capture structural variation. 2) Naive recognition methods are used, such as the nearest neighbor method. In this paper, we propose strengthening the recognition performance of these models as well as their ability to capture structural variation. The proposed method constructs a large number of graph models and trains decision trees using the models. This paper makes two main contributions. The first is a novel graph model that can quickly perform calculations, which allows us to construct several models in a feasible amount of time. The second contribution is a novel approach to structural data recognition: graph model boosting. Comprehensive structural variations can be captured with a large number of graph models constructed in a boosting framework, and a sophisticated classifier can be formed by aggregating the decision trees. Consequently, we can carry out structural data recognition with powerful recognition capability in the face of comprehensive structural variation. The experiments shows that the proposed method achieves impressive results and outperforms existing methods on datasets of IAM graph database repository. ✦ • T. Miyazaki and S. Omachi are with the

Graph Neural Networks with Multiple Feature Extraction Paths for Chemical Property Estimation

“…For the estimation of chemical properties, various models exist [ 19 , 20 , 21 , 22 , 23 , 24 ]. Directed graphs are used to reduce computation and update node features [ 22 , 23 ].…”