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|
%global _empty_manifest_terminate_build 0
Name: python-quickgraph
Version: 0.45
Release: 1
Summary: A Python package to view the skeleton of a social graph quickly.
License: MIT License
URL: https://gongqingyuan.wordpress.com/
Source0: https://mirrors.aliyun.com/pypi/web/packages/bd/cc/099d303ced7dc3e87a3f9a82d8bfb0f7cb2e7ca05ced62dfe91f2a50a8fc/quickgraph-0.45.tar.gz
BuildArch: noarch
%description
## 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
`pip install python-igraph`
to install the iGraph library
Run `pip install leidenalg`
to help the modularity related analysis
Note: Please change to `pip3 install` if you are using Apple M1 Chip
## Functions
quickgraph.info(G) returns the the basic information of a graph and plots the CDF of selected metrics.
quickgraph.LCC_analysis(G) characterizes the largest connected component (LCC) of the input graph G on selected metrics.
## Example
We utilize the SCHOLAT Social Network dataset as one example.
https://www.scholat.com/research/opendata/#social_network
```python
>>> import quickgraph
>>> quickgraph.demo()
Number of Nodes: 16007, Number of Edges: 202248
Avg. degree: 25.2699, Avg. clustering coefficient: 0.5486
Modularity (Leidenalg): 0.8651, Modularity (Label_Propagation): 0.8372
Number of connected components: 5423, Number of nodes in LCC: 9583 ( 59.8676 %)
Time (G_info): 4.675
LCC: Avg. degree = 40.023, Avg. clustering coefficient = 0.625, Modularity (Leidenalg): 0.8551, Modularity (Label_Propagation): 0.8209
(rough) shortest path length = 1 : 1 ( 0.1 %), 2 : 26 ( 2.6 %), 3 : 98 ( 9.8 %), 4 : 162 ( 16.2 %), 5 : 133 ( 13.3 %), 6 : 65 ( 6.5 %), 7 : 12 ( 1.2 %), 8 : 3 ( 0.3 %), Avg. shortest path length = 4.316
Time (LCC): 1.907
```
# License
See the LICENSE file for license rights and limitations (MIT).
%package -n python3-quickgraph
Summary: A Python package to view the skeleton of a social graph quickly.
Provides: python-quickgraph
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-quickgraph
## 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
`pip install python-igraph`
to install the iGraph library
Run `pip install leidenalg`
to help the modularity related analysis
Note: Please change to `pip3 install` if you are using Apple M1 Chip
## Functions
quickgraph.info(G) returns the the basic information of a graph and plots the CDF of selected metrics.
quickgraph.LCC_analysis(G) characterizes the largest connected component (LCC) of the input graph G on selected metrics.
## Example
We utilize the SCHOLAT Social Network dataset as one example.
https://www.scholat.com/research/opendata/#social_network
```python
>>> import quickgraph
>>> quickgraph.demo()
Number of Nodes: 16007, Number of Edges: 202248
Avg. degree: 25.2699, Avg. clustering coefficient: 0.5486
Modularity (Leidenalg): 0.8651, Modularity (Label_Propagation): 0.8372
Number of connected components: 5423, Number of nodes in LCC: 9583 ( 59.8676 %)
Time (G_info): 4.675
LCC: Avg. degree = 40.023, Avg. clustering coefficient = 0.625, Modularity (Leidenalg): 0.8551, Modularity (Label_Propagation): 0.8209
(rough) shortest path length = 1 : 1 ( 0.1 %), 2 : 26 ( 2.6 %), 3 : 98 ( 9.8 %), 4 : 162 ( 16.2 %), 5 : 133 ( 13.3 %), 6 : 65 ( 6.5 %), 7 : 12 ( 1.2 %), 8 : 3 ( 0.3 %), Avg. shortest path length = 4.316
Time (LCC): 1.907
```
# License
See the LICENSE file for license rights and limitations (MIT).
%package help
Summary: Development documents and examples for quickgraph
Provides: python3-quickgraph-doc
%description help
## 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
`pip install python-igraph`
to install the iGraph library
Run `pip install leidenalg`
to help the modularity related analysis
Note: Please change to `pip3 install` if you are using Apple M1 Chip
## Functions
quickgraph.info(G) returns the the basic information of a graph and plots the CDF of selected metrics.
quickgraph.LCC_analysis(G) characterizes the largest connected component (LCC) of the input graph G on selected metrics.
## Example
We utilize the SCHOLAT Social Network dataset as one example.
https://www.scholat.com/research/opendata/#social_network
```python
>>> import quickgraph
>>> quickgraph.demo()
Number of Nodes: 16007, Number of Edges: 202248
Avg. degree: 25.2699, Avg. clustering coefficient: 0.5486
Modularity (Leidenalg): 0.8651, Modularity (Label_Propagation): 0.8372
Number of connected components: 5423, Number of nodes in LCC: 9583 ( 59.8676 %)
Time (G_info): 4.675
LCC: Avg. degree = 40.023, Avg. clustering coefficient = 0.625, Modularity (Leidenalg): 0.8551, Modularity (Label_Propagation): 0.8209
(rough) shortest path length = 1 : 1 ( 0.1 %), 2 : 26 ( 2.6 %), 3 : 98 ( 9.8 %), 4 : 162 ( 16.2 %), 5 : 133 ( 13.3 %), 6 : 65 ( 6.5 %), 7 : 12 ( 1.2 %), 8 : 3 ( 0.3 %), Avg. shortest path length = 4.316
Time (LCC): 1.907
```
# License
See the LICENSE file for license rights and limitations (MIT).
%prep
%autosetup -n quickgraph-0.45
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-quickgraph -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.45-1
- Package Spec generated
|
