Understanding Convolutions on Graphs

Many systems and interactions - social networks, molecules, organisations, 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 extracting and utilizing features from the underlying graph, GNNs can make more informed predictions about entities in these interactions, as compared to models that consider individual entities in isolation. In this article, we will illustrate the challenges of computing over graphs, describe the origin and design of graph neural networks, and explore the most popular GNN variants in recent times.

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