GNNs: An Introduction to Graph Neural Networks

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Graph neural networks (GNNs) have recently become widely applied graph-analysis tools as they help capture indirect dependencies between data elements. Take this course to learn how to transform graph data for use in GNNs. Explore the use cases for machine learning in analyzing graph data and the challenges around modeling graphs for use in neural networks, including the use of adjacency matrices and node embeddings. Examine how a convolution function captures the properties of a node and those of its neighbors. While doing so explore normalization concepts, including symmetric normalization of adjacency matrices. Moving along, work with the Spektral Python library to model a graph dataset for application in a GNN. Finally, practice defining a convolution function for a GNN and examine how the resultant message propagation works. Upon completion you'll have a clear understanding of the need for and challenges around using graph data for machine learning and recognize the power of graph convolutional networks (GCNs).