Metadata-Version: 2.1
Name: quickgraph
Version: 0.27
Summary: A Python package to view the skeleton of 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
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
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 Qingyuan Gong, Fudan University (gongqingyuan@fudan.edu.cn)

## Before Installation

Please upgrade to Python 3.5

## System Requirements

We have tested QuickGraph on both MacOSX (version 11.5.1) and Ubuntu (Version: 20.04 LTS). 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 modularity related analysis 

## Functions
quickgraph.info(G) returns the the basic information of a graph and plots the CDF of selected metrics. 

quickgraph.LCC(G) characterizes the largest connected component (LCC) on selected metrics. 

## 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)
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
```

# License

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



