Summary
In this chapter, we learned about graph-based algorithms. This chapter used different techniques of representing, searching, and processing data represented as graphs. We also developed skills to be able to calculate the shortest distance between two vertices, and we built neighborhoods in our problem space. This knowledge should help us use graph theory to address problems such as fraud detection.
In the next chapter, we will focus on different unsupervised machine learning algorithms. Many of the use-case techniques discussed in this chapter complement unsupervised learning algorithms, which will be discussed in detail in the next chapter. Finding evidence of fraud in a dataset is an example of such use cases.
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