The architecture of this network analysis has three stages: data cleaning, generating graphs, and plotting counts of interest. Each file ending in cleaner.py creates pandas dataframes with columns: 'u', 'v', 'time', 'u_type', 'v_type', sorted by 'time'. The Grapher generates graphs from these dataframes by iteratively going through each row with edge information and counting if the edge is bichromatic or closes a wedge, saving the graphs and counts. The Plotter plots the counts saved by the Grapher. The script in run_full_analysis_on_govtrack_data.py shows an example of how to run this analysis on the govtrack bill cosponsorship dataset.
Centrality analysis is in both bill_cosponsorship_analysis.ipynb and stochastic_block_model.ipynb. SBM verifies theoretical counts against actual counts from a sample stochastic block model.
The variable names are consistent with the mathematical notation here and here.