June 2024
Intermediate to advanced
290 pages
7h 52m
English
Content preview from Modern Graph Theory Algorithms with Python
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2
Wrangling Data into Networks with NetworkX and igraph
In this chapter, we will introduce many types of data that are common in analytics projects, including spatial data, temporal data, and spatiotemporal data. Because these data structures do not exist as networks, they must be preprocessed into networks for further analytics. In future chapters, we will detail strategies for optimal preprocessing, but the goal of this chapter is to get familiar with the basics and how preprocessing works with the NetworkX and igraph packages.
We’ll consider many real-world problems in this chapter and the remaining chapters to build intuition around data that can be reformatted and analyzed as a network science problem. Oftentimes, network-based algorithms ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.