Bullying Detection through Graph Machine Learning : Applying Neo4j’s Unsupervised Graph Learning Techniques to the Friends Dataset

Abstract: In recent years, the pervasive issue of bullying, particularly in academic institutions, has witnessed a surge in attention. This report centers around the utilization of the Friends Dataset and Graph Machine Learning to detect possible instances of bullying in an educational setting. The importance of this research lies in the potential it has to enhance early detection and prevention mechanisms, thereby creating safer environments for students. Leveraging graph theory, Neo4j, Graph Data Science Library, and similarity algorithms, among other tools and methods, we devised an approach for processing and analyzing the dataset. Our method involves data preprocessing, application of similarity and community detection algorithms, and result validation with domain experts. The findings of our research indicate that Graph Machine Learning can be effectively utilized to identify potential bullying scenarios, with a particular focus on discerning community structures and their influence on bullying. Our results, albeit preliminary, represent a promising step towards leveraging technology for bullying detection and prevention.