http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
complete guide to graph representation learning with case studies
"This book provides a concise understanding of the subject of graph representation learning (GRL) which is a rapidly advancing field in the domain of machine learning. It includes such aspects as the basic concepts, state-of-the-art techniques, and real world applications of GRL which enable the reader to progress from a fundamental understanding of the approach to mastering its application. The authors also cover the topics of graph embedding methods, graph neural network (GNN) -based approaches and the latest trends in GRL such as deep learning, transfer learning, graph pooling, alignment, matching and graph machine learning. The book also includes examples of real-world applications of graph learning methods with real-world case studies in which the presented methods can be utilized"-- Provided by publisher.
E. Chandra Blessie, Pethuru Raj Chelliah, B. Sundaravadivazhagan.