Graph Neural Networks (GNNs) are gaining recognition in the machine learning community due to their potential for solving complex tasks, particularly in social networks, drug discovery, and recommendation systems. Unlike traditional neural networks, GNNs can operate on interconnected data with varying size, patterns, and complexity, allowing them to capture relationships between entities in a graph and perform tasks that require reasoning about network structure. GNNs are useful for dealing with complex systems that have interconnected components, making them a promising candidate for modeling many real-world problems. A blog series will explore the practical application of GNNs and related Graph ML, covering topics such as the promises and pitfalls of GNNs, Neo4j Graph Data Science, and alternatives to GNNs.