Understanding Convolutions on Graphs

Understanding the building blocks and design choices of graph neural networks. This article is one of two Distill publications about graph neural networks. Take a look at A Gentle Introduction to Graph Neural Networks Many systems and interactions - social networks, molecules, organizations, citations, physical models, transactions - can be represented quite naturally as graphs. How can we reason about and make predictions within these systems? One idea is to look at tools that have worked well in other domains: neural networks have shown immense predictive power in a variety of learning tasks. However, neural networks have been traditionally used to operate on fixed-size and/or regular-structured inputs (such as sentences, images and video). This makes them unable to elegantly process graph-structured data. Graph neural networks (GNNs) are a family of neural networks that can operate naturally on graph-structured data. By…