WITHDRAWN: IEER: Inferring Embedded Email Roles

Abstract

In this study, we investigate the use of representation learning techniques for solving complex problems represented as graphs. To this end, we propose a novel method for learning graph representations called Inferring Embedded Email Roles (IEER). Our method generates low-dimensional vectorized features by acquiring local structural information for each vertex in the graph. Unlike existing approaches that have focused on incorporating various graph attributes, our method takes a unique approach to improving the performance of the base framework, EMBedding Email-based Roles (EMBER). This research aims to understand the effectiveness of an embedding technique for generating graph representations and its suitability for replacing the base framework technique. We identify an embedding technique that significantly improves the performance of EMBER, resulting in a 94\% accuracy improvement using the Permutation Invariant technique, which has been shown to outperform matrix factorization techniques in capturing structural behaviors. This technique has been shown to outperform matrix factorization techniques in dealing with structural behaviors. Additionally, our method is scalable and linear concerning the number of vertices in the graph.