Classifying Documents on a Graph using GNNs - Avi Aminov | PyData Global 2021

Classifying Documents on a Graph using GNNs Speaker: Avi Aminov Summary The usage of graphs to model and solve ML problems is becoming very popular. In this talk we’ll review example implementations of Graph ML and how they assist by generating models that take advantage of the individual data point features combined with the graph structure. As an elaborate example, I will present how we use Graph ML to classify document sensitivity without looking at the content. Description Many of the problems we face as scientists can be natively modelled on graph structures that captures both the attributes of objects (nodes) as well as the relationships between them (edges). Such structures include social networks, computer networks, code execution flows, molecular structures, and many more. We will skim briefly through examples (from the last decade) of how Graph ML has been applied successfully to problems including node classification, edge (link) prediction, and whole graph predictions (e