Graph Machine Learning: From Classical Approach to Graph Neural Network
Over the last few years, we've seen a rise in graph algorithms in a lot of use cases. One overlooked problem is that we lack a map to orient ourselves in this changing technological world. In this talk, we'll explain the logical steps and algorithms used for graph-based machine learning paths. You'll go on a journey starting with classical machine learning with hand-written graph features and machine learning models, moving on to node embedding starting from node2vec and arriving at graphSage, and finally reaching graph neural networks.
Julien Genovese, Graph Data Scientist at Data Reply