An Attempt at Demystifying Graph Deep Learning - Eric Ma | PyData Global 2021

An Attempt at Demystifying Graph Deep Learning Speaker: Eric Ma Summary In this talk, I will attempt to demystify the core ideas behind graph deep learning with lots of pictures and a minimum number of equations. Description This talk will follow a four-part structure. Firstly, we will introduce graphs and how they can be represented as arrays. Then, we will walk through what message passing is, and how it also has a linear algebra interpretation. Thirdly, we will see how we can embed the message passing operation inside a neural network, thus giving us a message passing neural network. We’ll also see how other network architectures come up. Finally, we will walk through learning tasks that involve graphs. In bullet point form: Graphs, networks, and their array representations Introduction to graphs How graphs can be represented as arrays Message passing Definition of the message passing operation Message passing operators beyond the adjacency matrix Embeddi