▶︎ CANCELED: IST Lunch Bunch

Graph tasks are ubiquitous, with applications ranging from
recommendation systems, to language understanding, to automation with
environmental awareness and molecular synthesis. A fundamental challenge
in applying machine learning to these tasks has been encoding
(representing) the graph structure in a way that ML models can easily
exploit the relational information in the graph, including node and edge
features. Until recently, this encoding has been performed by factor
models (a.k.a. matrix factorization embeddings), which arguably originated in 1904 with
Spearman's common factors. Recently, however, graph neural networks have
introduced a new powerful way to encode graphs for machine learning
models. In my talk, I will describe these two approaches and then
introduce a unifying mathematical framework using group theory and causality
that connects them. Using this novel framework, I will introduce new practical
guidelines to generating and using node embeddings and graph representations,
which fixes significant shortcomings of the standard operating procedures used
today.

This is joint work with Ryan Murphy, Bala Shrinivasan, and Vinayak Rao.