Persistence diagrams are useful in that they allow us to identify patterns in data that exist in small regions of interest (usually spatial, here temporal) or large regions of interest; either of these regimes may be most useful for further analysis depending on the topic at hand (Stolz-Pretzer ( )). As samples of trajectories are taken with finite gaps of time between them and disk space is not infinite, the temporal sampling rate of a trajectory is a controllable variable. Similarly, the sampling rate for our persistence diagram analysis can be some integer fraction of the sampling rate from the trajectory. Analysing networks at different sampling rates can reveal important patterns in the data, for example low-frequency sampling rates can smooth out ephemeral patterns in the data, and high-frequency sampling rates can draw attention to short-lived phenomena. Alternatively, low-frequency sampling rates can save the computational expense of tracking many unique and short-lived rings. Importantly, the similarity between Figure ) reinforces the idea that high resolution temporal data is not required for persistence diagrams to be a useful analysis tool. Even at the coarsest sampling frequency, key behaviours can still be seen in the persistence diagram and the bars can show which regions of time are interesting enough to study in more detail.