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It was a few weeks ago that I became acquainted with Python’s graphing library, NetworkX. I learned of this library because I was working on a machine learning project that involved making predictions on nodes and edges, being integral parts of graph theory.
What is graph theory and how does it relate to machine learning?
Graph theory is a mathematical framework that deals with the study of graphs, which are mathematical structures consisting of vertices (also known as nodes) and edges (also known as links or connections) that connect pairs of vertices. In the context of machine learning, graph theory plays a crucial role in various applications, especially when dealing with structured data.
Graphs can be used to represent complex relationships and dependencies between data points, making them an effective tool for modelling and analysing real-world systems. Here’s how graph theory relates to machine learning:
- Representation: Graphs provide a powerful way to represent data with interconnected relationships. In machine learning, graphs can represent various types of data, such as social networks, recommendation systems, citation networks, biological networks, and more. Each node in the graph can represent an entity or data point, while edges capture the relationships between them.
