Graph Neural Networks, also known as GNNs, have seen a meteoric rise in popularity over the past few years due to its capacity to analyse data that is shown in the form of graphs. GNNs have been put to use in a broad variety of industries, including social network research, the search for new drugs, recommender systems, and traffic prediction, to mention just a few examples. GNNs are becoming increasingly popular, which has resulted in an increased interest in the issue among scholars and practitioners who are interested in better comprehending the fundamental ideas and procedures that underpin GNNs. The book “Concepts and Techniques of Graph Neural Networks” is a reference to the concepts and procedures that are utilised in Graph Neural Networks (GNNs). GNNs have developed as an effective method for modelling complicated structured data, such as social networks, protein structures, and traffic patterns, among other applications. Applications have been identified for them in a broad variety of domains, such as drug discovery, computer vision, natural language processing, and recommender systems. GNNs are especially helpful for solving situations in which the data is modelled as a graph, in which the nodes of the graph represent entities and the edges reflect the relationships between those entities.