Success in machine learning is all about the data we have available. Using graph representations and graph analytics enables organisations to understand their data in new and powerful ways. Many datasets are naturally graphs, and some benefit from being treated as a graph. There is significant potential then for graph analytics and machine learning to be used together. This session will cover the intersection of these two fields at a high level. We will introduce basic concepts and explore three areas in which graph-based machine learning is seeing traction: measures on graphs for features and constraints, semi-supervised learning with graphs, and graph-based deep learning.
You can view Steve’s slides below:
You can view Steve’s presentation below: