Deep Learning: Graph Neural Networks (GNN) for Information Extraction with PyTorch

In this talk, I will provide a primer on a class of deep learning models known as graph neural networks (GNNs). These models generalize the well-known convolutional neural networks (CNNs) ubiquitous in computer vision, and the approach of the talk will emphasize that analogy. Specifically, after a quick general recap on graphs, we will see how to think of an image as a graph where nodes represent pixels and edges represent the relationship of being a neighbor. After expressing the formulas for a CNN in terms of this graph, it is not hard to imagine ways to pass from the graph of an image to arbitrary graphs. This way, one naturally arrives at a basic GNN architecture. Other recent advances, for instance graph attention networks, can also be surveyed, time permitting. We will then survey the existing Python implementations and supporting libraries, with a focus on the PyTorch framework and on the PyTorch Geometric library in particular. Next, I will turn to applications in the field of NLP,