Semantic Graph Neural Network: A Conversion from Spam Email Classification to Graph Classification
Weisen Pan, Jian Li, Yixing Gao, Liexiang Yue, Yan Yang, Lingli Deng, Chao Deng
Abstract
In this study, we propose a method named Semantic Graph Neural Network (SGNN) to address the challenging task of email classification. This method converts the email classification problem into a graph classification problem by projecting email into a graph and applying the SGNN model for classification. The email features are generated from the semantic graph; hence, there is no need of embedding the words into a numerical vector representation. The method performance is tested on the different public datasets. Experiments in the public dataset show that the presented method achieves high accuracy in the email classification test against a few public datasets. The performance is better than the state-of-the-art deep learning-based method in terms of spam classification.