Know Your Neighbours: Machine Learning on Graphs
YOW! Data 2018
Machine learning has become ubiquitous in many applications. There are many accessible tools available to apply standard machine learning models to make predictions on data.
Typically, machine learning problems aim to predict something about an entity using some data about each entity. However, in the real world, entities - people, places or things - are connected to each other and can have complex interactions with their neighbours. We can use this information to improve our predictions, and to gain more insight into the network structure and how entities can affect each other.
This talk will introduce machine learning on graphs, give some examples where graph approaches give large improvements over standard machine learning techniques, and will demonstrate some tools that make graph machine learning more approachable.
Sr. Research Engineer