Metadata-Version: 2.1
Name: quickgraph
Version: 0.18a0
Summary: Python package for overviewing a social graph quickly.
Home-page: https://gongqingyuan.wordpress.com/
Author: Mobile Systems and Networking Group, Fudan University
Author-email: gongqingyuan@fudan.edu.cn
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE

#Introduction 

QuickGraph library can help you get a quick overview of a social graph in an extremely convenient way. QuickGraph will show the basic information of a graph, plot the CDF of selected metrics, characterize the largest connected component (LCC).

#Overview

QuickGraph library can help you get a quick overview of a social graph in an extremely convenient way.
Show the basic information of a graph, plot the CDF of selected metrics, characterize the largest connected component (LCC), compute representative structural hole related indexes.  
Copyright (C) <2021-2026> by Yang Chen, Fudan University (chenyang03@gmail.com)

#Before Installation

Please update to Python 3.5

#System Requirements

We have tested QuickGraph on both MacOSX (version xx) and Ubuntu (Version: xx). This library have not been tested on other platforms.

#Usage

Please run the following commond and install the dependent libiraires:
Run 
‘conda config --add channels conda-forge’
‘conda update –all’ to make the libraries fit to the operation system
Run ‘conda install networkx’ to install the networkx library
Run ‘conda install python-louvain’ to help the structural hole related analysis 

#Example
'''
>>> import quickgraph as qg
>>> import networkx as nx
>>> G = nx.les_miserables_graph()
>>> qg.info(G)
Number of Nodes: 77, Number of Edges: 254
Avg. degree: 6.5974, Avg. clustering coefficient: 0.5731, Modularity (Louvain) = 0.5663
Number of connected components: 1, Number of nodes in LCC: 77 ( 100.0 %)
>>> qg.LCC_analysis(G,1,1,1)
LCC: Avg. degree = 6.5974, Avg. clustering coefficient = 0.5731, Modularity (Louvain) = 0.5663
(rough) shortest path length = 0 : 3 ( 0.3 %), 1 : 34 ( 3.4 %), 2 : 182 ( 18.2 %), 3 : 205 ( 20.5 %), 4 : 72 ( 7.2 %), 5 : 4 ( 0.4 %), Avg. shortest path length = 2.642
'''

#Gallery

Here are some figures generated by QuickGraph, including the CDF of degree, clustering coefficient and the size of top 10 connected components.

#License

See the LICENSE file for license rights and limitations (MIT).



