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.
Germany
Slovakia
Canada
Brazil
Singapore
Hungary
Philippines
Mexico
Thailand
Ukraine
Luxembourg
Estonia
Lithuania
Norway
Chile
United States
Great Britain
India
Spain
South Korea
Ecuador
Colombia
Taiwan
Switzerland
Indonesia
Cyprus
Denmark
Finland
Poland
Malta
Czechia
New Zealand
Austria
Turkey
France
Sweden
Italy
Egypt
Belgium
Portugal
Slovenia
Ireland
Romania
Greece
Argentina
Malaysia
South Africa
Netherlands
Bulgaria
Latvia
Australia
Japan
Russia