GraphConnect 2020

When do you use graphs for machine learning, what domains can they be used in, and how do you get started. Real world examples and use cases to show the steps from getting started with a knowledge graph through to graph native learning. Graphs - or information about the relationships, connection, and topology of data points - are transforming machine learning. We'll walk through real world examples of how to get transform your tabular data into a graph and how to get started with graph AI.

This talk will provide an overview of how we to incorporate graph based features into traditional machine learning pipelines, create graph embeddings to better describe your graph topology, and give you a preview of approaches for graph native learning using graph neural networks. We'll talk about relevant, real world case studies in financial crime detection, recommendations, and drug discovery.

This talk is intended to introduce the concept of graph based AI to beginners, as well as help practitioners understand new techniques and applications. Key take aways: how graph data can improve machine learning, when graphs are relevant to data science applications, what graph native learning is and how to get started.