Graph convolutional neural networks for distributed recommender and prediction systems

Deep neural networks have gained prominence in recent years in areas such as image processing, computer vision, speech recognition, machine translation, self-driving vehicles, and healthcare. Deep learning, a subset of machine learning and AI, is revolutionising our lives. Graph deep learning is a new field. For graph-structured data, newly developed GNNs were developed. While GNNs outperform conventional approaches in tasks like semi-supervised node classification, their application to other graph learning problems have not been studied or their performance isn’t acceptable. This paper explores graph deep learning in more detail. There are many more graph learning challenges that can be solved using graph neural networks.