Tutorials# Step-by-step tutorials demonstrating how to use each category of backbone extraction methods. Statistical Backbone Extraction Setup Disparity filter Noise-corrected filter Marginal likelihood filter ECM filter LANS filter Multiple linkage analysis Comparing all statistical methods Proximity-Based Edge Scoring Setup Local proximity methods Quasi-local methods Filtering by proximity Comparing proximity methods Structural Backbone Methods Setup Simple filters Linkage and centrality filters Shortest-path methods Normalization Index-based Community-based Constrained methods Comparing structural methods Bipartite Projection Backbones What is a bipartite projection backbone? Setup Weighted projections SDSM: Stochastic Degree Sequence Model FDSM: Fixed Degree Sequence Model Additional null models Comparing SDSM and FDSM Partition selection note References Comparing Multiple Methods Setup Creating multiple backbones Using compare_backbones Custom measures Consensus backbone Interpreting results Unweighted Graph Sparsification Setup The sparsify pipeline Convenience wrappers Comparing unweighted methods Les Miserables Benchmark Setup Filtered Edge Counts Reproducibility Script Graph Comparison Gallery Les Miserables (Non-Bipartite Methods) Davis Southern Women (Bipartite Methods)