A deeper dive into spreading on networks

Now that we understand a bit about how networks can change over time, let’s see how we can represent these networks in Python and compute spreading processes such as epidemic models over time on a dynamic network. Python provides several tools to help us both visualize and analyze dynamic networks as we have in prior chapters. Let’s get started.

Dynamic network introduction

We can track changes over a network through time by keeping track of connections at different time points. Let’s consider a pride of lions in which individuals interact periodically over the course of the day. Figure 8.4 shows this example network with timestamps to track edges:

Figure 8.4 – An example of a dynamic network with timestamps

Figure 8.4 shows an example of a dynamic lion network with an evolving structure of edges and vertices that can be stamped with time information. Python provides a handy package...