Learning Graph Representations
This paper provides an overview of existing GNN methods for researchers and practitioners interested in learning graph representations for various graph-based machine learning problems.
This paper discusses various Graph Neural Network (GNN) architectures, including graph convolutional neural networks, graph autoencoders, and spatio-temporal graph neural networks. These methods are used to learn lower-dimensional representations of graph data, which can then be applied to downstream machine learning tasks such as graph classification, node classification, and link prediction.
Social and information networks are gaining huge popularity recently due to their various applications. Knowledge representation through graphs in the form of nodes and edges should preserve as many characteristics of the original data as possible. Some of the interesting and useful applications on these graphs are graph classification, node classification, link prediction, etc. The Graph Neural Networks have evolved over the last few years. Graph Neural Networks (GNNs) are efficient ways to get insight into large and dynamic graph datasets capturing relationships among billions of entities also known as knowledge graphs. In this paper, we discuss the graph convolutional neural networks graph autoencoders and spatio-temporal graph neural networks. The representations of the graph in lower dimensions can be learned using these methods. The representations in lower dimensions can be used further for downstream machine learning tasks.