Graph Neural Networks (GNNs) are a type of artificial neural network used for processing complex structured data such as graphs, networks, and trees. GNNs are typically used for tasks such as graph classification, node classification, link prediction, and clustering. GNNs are part of the broader category of neural networks known as structural deep learning methods.
Unlike traditional neural networks, which process data in a flattened vector form, GNNs leverage graph convolutional layers to analyze data in its native format (i.e., a graph). These layers are specifically designed for propagating information through complex structures and allow for the incorporation of graph-structured information into the learning process. GNNs are often employed in the development of complex AI-powered solutions such as those used in recommender systems and natural language processing.
GNNs are typically composed of graph convolutional layers, which are designed to process complex data by propagating information through each node in the graph. These layers are trained using an objective function that minimizes the total error of a model by back-propagating the gradients through the graph. The output of the convolutional layer is often used to generate a graph-structured representation of the input data that is more easily interpreted by conventional machine learning algorithms.
GNNs are an exciting and rapidly emerging field of artificial intelligence. The flexibility and representational power offered by GNNs have enabled researchers to develop robust AI-powered applications in a variety of fields, such as natural language processing, recommendation systems, and computer vision.
