In the past decade, we have seen remarkable breakthroughs in the areas of machine and deep learning, and their applications in a wide range of tasks, from image classification & video processing to speech recognition & natural language understanding. The data in these tasks are typically represented in the Euclidean space (i.e. they follow the rules of Euclidean geometry, e.g. the shortest distance between two points is a straight line). However, there are a number of applications where data are generated from non-Euclidean domains and are best represented as graphs with complex relationships (e.g. client accounts in a bank, interactions between these accounts, and the inflow and outflow of assets from these accounts). Consequently, many studies on extending deep learning techniques for graph data have emerged. There are indications that these emerging techniques perform significantly better than traditional methods on non-Euclidean data (e.g. rules based or statistical approach). Given their relevance to areas such as prevention of money laundering and financial crime, in this post, I have provided links to a couple of introductory talks on this topic. Enjoy!