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IPF for dynamic network inference

This respository contains code to run all experiments for "Inferring dynamic networks from marginals with iterative proportional fitting" by Serina Chang*, Frederic Koehler*, Zhaonan Qu*, Jure Leskovec, and Johan Ugander, published in ICML 2024. The main files are:

  • test_ipf.py: functions to run IPF, test and enable IPF convergence (our ConvIPF algorithm), and run the equivalent Poisson regression.
  • experiments_with_data.py: functions to run experiments with data - synthetic data, SafeGraph mobility data, and CitiBike bikeshare data
  • icml_experiments.ipynb: notebook to recreate all results and figures reported in the paper.

The other files are imported from other repos (Chang et al., 2021) to use SafeGraph mobility data.