quickgraph 0.19

Python package for overviewing a social graph quickly.

#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 ([e-mail])

#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).