Geometric Deep Lean Learning: Evaluation Using a Twitter Social Network

Abstract

The goal of this work is to evaluate a deep learning algorithm that has been designed to predict the topological evolution of dynamic complex non-Euclidean graphs in discrete–time in which links are labeled with communicative messages. This type of graph can represent, for example, social networks or complex organisations such as the networks associated with Industry 4.0. In this paper, we first introduce the formal geometric deep lean learning algorithm in its essential form. We then propose a methodology to systematically mine the data generated in social media Twitter, which resembles these complex topologies. Finally, we present the evaluation of a geometric deep lean learning algorithm that allows for link prediction within such databases. The evaluation results show that this algorithm can provide high accuracy in the link prediction of a retweet social network.