This repo contains the code for analyzing scale-free patterns in networks as described in this paper. The data sets used in the paper are in the degreesequences directory.
If you use this code in your research, please cite this repo and/or the paper (linked above):
Anna D. Broido & Aaron Clauset, "Scale-free networks are rare", Nature Communications 10, 1017 (2019).
There are two ways to use this repo:
-
extract degree sequences from network data in the form of gml files and then analyze these for scale-free patterns
-
given degree sequences in the appropriate format (examples in the degree sequences folder), sort them into scale-free categories. Without gml information, this version of the pipeline treats each degree sequences as belonging to a unique network.
The code is written in Python2 and will not work in Python3. Additionally, the following packages must also be installed:
- NumPy
- Pandas
- SciPy
- mpmath
- python-igraph
import sys
import pandas as pd
sys.path.append('../code/')
import sfanalysis as sf
# location of gml files to analyze
gml_dir = 'gmls/'
# location to write degree sequences
deg_dir = 'degseqs/'
# make catalog of gmls and write degree sequence files
# each row of deg_df is a degree sequence file
deg_df = sf.write_degree_sequences(gml_dir, deg_dir)
# analyze all degree sequences (this will take a while for many or large data sets)
analysis_df = sf.analyze_degree_sequences(deg_dir, deg_df)
# categorize networks (by unique gml file) into scale-free categories
hyps_df = sf.categorize_networks(analysis_df)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent