%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 - 0.45-1 - Package Spec generated