Distance Backbone Synthesis

Please cite the following paper if you build upon this work:

Distance Backbones Optimize Spreading Dynamics and Centrality Ranks in the Sparsification of Complex Networks Bernardo Pereira, Felipe Xavier Costa, and Luís M. Rocha
arXiv:2603.14564 (2026) Read the Paper | DOI Link

Quick Usage Guide

Start with a proximity network then the pipeline follows:

• add_edge_parameter (distortion, effective resistance, disparity, backbone size)

• add_lambdas (paralelized)

• create_subgraphs (given threshold)

• eigenvector_centrality_get_data (compute centrality)

• si_get_data (do si simulations)

• si_manipulate_data (errors of si simulations)

• create_auc_data (compute AUC)

• eigenvector_centrality_optimal_get_data (optimal)

• si_optimal_get_data (optimal)

The US networks are so big that the eigenvector centrality computation is done every 5 edges instead of every edge.