This talk will walk through a node embedding algorithm called HashGNN. Node embedding algorithms provide an important bridge between graphs and traditional machine learning. Such algorithms construct for each node in a graph an associated vector which can capture local graph structure and properties. We will discuss why HashGNN is a useful embedding algorithm, its implementation in GDS which is an extension to heterogenous graphs, some intuition behind it, how it compares to Graph Neural Networks which inspired HashGNN, and more.