Graph Neural Networks for Link Prediction

Graph Neural Networks (GNNs) belong to a class of deep learning methods that are specialized for extracting critical information and making accurate predictions on graph representations. Researchers have been striving adapt neural networks process data over decade. GNNs found practical applications in various fields, including physics simulations, object detection, recommendation systems. Predicting missing links graphs is crucial problem scientific fields because real-world frequently incompletely observed. This task, also known as link prediction, aims predict the existence or absence graph. tutorial designed researchers who no prior experience with will provide an overview prediction task. In addition, we discuss further reading, applications, most commonly used software packages frameworks.