  Islamophobic language is a prevalent challenge on online social interaction
platforms. Identifying and eliminating such hatred is a crucial step towards a
future of harmony and peace. This study presents a novel paradigm for
identifying and explaining hate speech towards Islam using graph neural
networks. Utilizing the intrinsic ability of graph neural networks to find,
extract, and use relationships across disparate data points, our model
consistently achieves outstanding performance while offering explanations for
the underlying correlations and causation.
