From the course: Learning Graph Neural Networks
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Approaches to graph machine learning
From the course: Learning Graph Neural Networks
Approaches to graph machine learning
- [Instructor] Before we zoom in and discuss graph neural networks in detail, let's talk about the different kinds of machine learning models that you can use with graphs. Machine learning approaches to graphs can be categorized into three broad categories. We first have the classic graph algorithms. We have graph algorithms that use representation learning. And finally, we have graph neural networks. Graph neural networks is the focus of this course, but we'll briefly discuss some of the other categories of ML models as well. Let's start with a discussion of classic graph algorithms. These are all algorithms that we've heard of with graphs. These refer to traditional methods developed in graph theory to analyze and process graphs without machine learning. In a very strict sense, these are not part of machine learning, but they're just graph algorithms. These classic graph algorithms include shortest path algorithms like Dijkstra's or Bellman-Ford to find the shortest path between…
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