Graph Neural Networks (GNN). Introduction

GNNs are a type of neural network that can process data with complex, non-Euclidean structure, such as graphs and networks. They have been widely used in AI and ML for tasks such as node classification, graph classification, and link prediction. One key area of focus has been on developing more efficient GNN architectures for large-scale graphs. This has included the development of hierarchical and modular GNNs, as well as the use of sparsification and approximation techniques to reduce the computational complexity of training and inference. Another area of focus has been on improving the ability of GNNs to capture long-range dependencies and higher-order connectivity patterns in graphs.