April 2019
Beginner
190 pages
4h 54m
English
Content preview from Network Science with Python and NetworkX Quick Start Guide
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Summary
This chapter has shown how networks that exist in time or space can be represented, analyzed, and visualized in NetworkX. Such networks have additional constraints imposed by the physical realities of time and space. Spatial networks can be visualized using the actual locations of nodes. Gravity models can be used to compensate for different lengths when comparing edge properties. Networks that change over time can be analyzed by creating snapshots, and can possibly link those snapshots into a layered network. This chapter gave examples of working with temporal and spatial networks using US air traffic data and historical data on Dutch Wikipedia articles. The next chapter will cover some advanced visualization techniques in NetworkX. ...
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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.