{"id":287,"date":"2012-11-02T16:34:47","date_gmt":"2012-11-02T20:34:47","guid":{"rendered":"http:\/\/www.michelecoscia.com\/?page_id=287"},"modified":"2026-08-13T03:32:54","modified_gmt":"2026-08-13T07:32:54","slug":"network-toolbox","status":"publish","type":"page","link":"https:\/\/www.michelecoscia.com\/?page_id=287","title":{"rendered":"Network Backboning"},"content":{"rendered":"
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MultiLayer Version<\/strong><\/h3>\n\n\n\n

Download Full Package<\/a><\/strong><\/p>\n\n\n\n

The archive contains all the data and code to reproduce the paper “Multilayer Network Backboning,” published in CIKM26.<\/p>\n\n\n\n

If you’re only interested in performing multilayer backboning, the minimum code needed is:<\/p>\n\n\n\n

import backboning as bb

edges_bb = bb.noise_corrected_ml(edges)<\/pre>\n\n\n\n
Assuming that “edges” is a pandas dataframe with a multilayer edgelist. Each row in the dataframe should contain one edge. The dataframe should have columns src, trg (for the nodes connected by the edge), layer (the layer id connecting the edges), and nij (the edge weight). src, trg, and layer can be arbitrary strings, nij should be a non-negative int (but it can be float as well). The “wiki_data\/edges.csv” file in the archive is an example of a well-formatted input file.<\/p>\n\n\n\n
Once you run it, you can still use the standard thresholding function to extract the backbone just like in the single layer example below.<\/p>\n\n\n\n
Download Full Package<\/a><\/strong><\/p>\n\n\n\n
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Single Layer Version<\/strong><\/h3>\n\n\n\n
Download Backboning Code and Data (Python 2, Networkx 1 version)<\/strong><\/a><\/p>\n\n\n\n
Backboning code for Python 3 & Networkx 2<\/strong><\/a><\/p>\n\n\n\n
The archive contains a Python module to perform network backboning, which is the filtering of non-significant edges from a very dense and noisy network.<\/p>\n\n\n\n
The module provides several utilities and the following methods:<\/p>\n\n\n\n