pyGAlib – Graph Analysis Library in Python / NumPy

pyGAlib is a library to generate and study graphs and complex networks in Python. It treats networks as adjacency matrices in order to take advantage of faster NumPy array manipulations. The library is easy to install, use, modify and extend.

Main features...

networks represented as adjacency matrices (rank-2 ndarrays);
extensive use of NumPy for high performance over pure Python code;
Some functions further boosted using Numba;
transparent and flexible: find which part of the code is doing what;
Python 3 compatible.

... and some limitations:

management of large networks due to NumPy dependence,
no graph visualization tools.

INSTALLATION

Installation of pyGAlib is simple, only the pip package manager is needed. To check whether pip is installed, open a terminal and type:

pip --help

NOTE: If you use Anaconda (or any other third-party package manager), we recommend to install the dependencies (python>=3.6, numpy>=1.6, scipy and numba) into the target environment using Anaconda before installing pyGAlib. Otherwise, pip will download and install those packages directly from PyPI as well, and you won't be able to manage them through Acanconda.

Installing from PyPI

pyGAlib is registered in the official Python Package Index, PyPI . To install, open a terminal window and type:

python3 -m pip install galib

To confirm the installation, open an interactive session (e.g., IPython or a Notebook) and try to import the library by typing import galib.

Direct installation from GitHub

If you have git installed, you may like to install pyGAlib directly from its GitHub repository. Open a terminal and type:

python3 -m pip install git+https://github.com/gorkazl/pyGAlib.git@master

This will only download and install the package (files in "src/galib/") into your current environment. If desired, additional files of the repository (e.g. the examples in the Examples/ folder) should be downloaded manually. You can choose to install the version in another branch by replacing the '@master' at the end of the command by '@branchname' of the desired branch.

Installing pyGAlib in editable mode

If you want to install pyGAlib such that you can make changes to it "on the fly" then, visit its GitHub repository https://github.com/gorkazl/pyGAlib/, select a branch and then click on the green "<> Code" button on the top right and select "Download ZIP" from the pop-up menu. Once downloaded, move the zip file to a target folder (e.g., "~/Documents/myLibraries/") and unzip the file. Open a terminal and cd to the resulting folder, e.g.,

cd ~/Documents/myLibraries/pyGAlib-master/

Once on the path (make sure it contains the pyproject.toml file), type:

python3 -m pip install -e .

Do not forget the "." at the end which means "look for the pyproject.toml file in the current directory." This will install pyGAlib such that every time changes are made to the package (located in the path chosen), these will be inmediately available. You may need to restart the IPython or Jupyter notebook session, though.

HOW TO USE pyGAlib

The library is organised in the following modules:

metrics.py: Common graph metrics (degrees, clustering, graph distance, etc)
models.py: Generation of synthetic networks and randomization.
tools.py: Miscelaneous helper functions.
metrics_numba.py: Uses the Numba package to accelerate calculation of some metrics.
models_numba.py: Uses the Numba package to accelerate generation of some graph models.
extra.py: Additional measures and functionalities related to network analysis.

Getting started

Since pyGAlib depends on NumPy, it is recommended to import NumPy first. Although this is not necessary for loading pyGAlib, NumPy functionalities and array manipulation will be often needed. Try importing pyGAlib:

>>> import numpy as np
>>> import galib

NOTE: Importing galib imports also all functions in module metrics.py into its namespace. The rest of modules are imported separately. Therefore, if the import is relative those functions can be called as, e.g.,

>>> import galib
>>> ... 
>>> deg = galib.Degree(net)
>>> C, Cnodes = galib.Clustering(net)

See that we did not have to call galib.metrics.Degree(net). In the case of an absolute import (using an asterisk *) all functions in metrics.py are imported to the base namespace:

>>> from galib import *
>>> ... 
>>> deg = Degree(net)
>>> C, Cnodes = Clustering(net)

Example

Let's generate a random graph following the Erdos-Renyi model, G(N,p), with N = 100 nodes and link probability p = 0.1:

>>> import galib
>>> import galib.models
>>> N = 100; p = 0.1
>>> net = galib.models.ErdosRenyiGraph(N, p, directed=False)

Here, net is the adjacency matrix of the random graph represented as a 2-dimensional NumPy array. Let's calculate some basic properties.

>>> galib.Density(net)
0.09838383838383838

As expected, the density of an Erdos-Renyi random graph is close to the p = 0.1 value given. We now calculate the degree of every node:

>>> deg = galib.Degree(net)
>>> deg
array([10,  7, 10, 10, 11,  7,  5, 11, 13, 12, 14, 13,  8, 10,  9,  8,  7,
   10, 11,  9, 11, 11,  8, 10,  5,  9, 13, 10, 13, 12, 12, 11, 11,  7,
   13, 11,  7, 10, 10,  6, 12, 10,  6, 10,  7,  6,  9, 10,  9,  9,  7,
    9,  8, 13, 10,  9,  7,  7, 11,  8, 13,  6,  7, 12, 14,  6,  5, 11,
    5, 12, 14, 14, 13,  8,  7, 12,  4, 19,  9, 13,  7, 10, 15, 15,  4,
    9,  7, 12,  7,  8, 12,  4, 11, 12,  6, 13,  6, 12, 16, 12])

The degree is returned as a numpy array of rank 1, of integer type. The function Clustering returns both the global clustering coefficient and the local clustering of every node:

>>> C, Cnodes = galib.Clustering(net)
>>> C
0.096051227321238
>>> Cnodes
array([0.13333333, 0.04761905, 0.04444444, 0.08888889, 0.10909091,
   0.19047619, 0.3       , 0.12727273, 0.08974359, 0.06060606,
  	... ... ...
  	... ... ...
   0.04545455, 0.        , 0.10909091, 0.13636364, 0.        ,
   0.07692308, 0.13333333, 0.09090909, 0.08333333, 0.10606061])

We compute the pair-wise graph distance between all the nodes using the Floyd-Warshall algorithm, which is returned as a matrix dij (numpy array of rank 2):

>>> dij = galib.FloydWarshall(net)
>>> avlen = (dij.sum() - dij.trace()) / (N*(N-1))
>>> avlen
2.248080808080808

NOTE: For calculating the distance matrix of larger networks please use the version of the function located in the module metrics_numba.py. Here, the function FloydWarshall_Numba() works the same but makes use of the Numba library to significantly speed the calculation.

Most network generators and graph metrics in pyGAlib work with directed graphs as well. Check for the optional parameter directed. Following the example above, we generate a directed Erdos-Renyi graph and calculate its input and output degrees for every node:

>>> net = galib.models.ErdosRenyiGraph(N, p, directed=True)
>>> galib.Density(net)
0.10272727272727272
>>> indeg, outdeg = galib.Degree(net, directed=True)
>>> indeg
array([17,  7,  9,  8, 11, 10,  9,  8, 13, 13,  5,  9, 13,  9, 10, 10, 13,
   10,  9,  9,  7, 11, 13, 10,  4, 15, 11, 11, 10,  6,  6,  8,  8,  8,
   11,  8,  4, 12,  8, 13, 13, 14, 12,  5,  6,  5, 16, 12,  5, 10,  9,
   13,  8,  9,  7,  8, 13, 14,  9, 18,  7, 11,  5,  4, 12,  8,  8, 10,
    7,  9, 15, 12, 14,  9, 15, 11, 13, 12, 15, 10, 11, 11, 15,  7, 10,
   13,  7, 14,  9, 16, 11, 11,  6, 18,  7,  4, 14, 12, 12, 10])
>>> outdeg
array([ 9, 10,  7,  9, 12,  9, 19,  9, 11, 16, 11, 12, 11, 15, 11,  6,  9,
    8, 11, 12,  9, 13,  9,  8, 11,  6,  7, 11, 11, 12, 10,  8, 11, 12,
   10, 12, 13,  8, 18, 11,  8, 13, 10,  8, 10, 10, 11,  8, 11, 11, 11,
   10, 11, 10,  9, 12,  6, 10,  7, 10, 10, 11, 15, 12, 11,  7, 10,  8,
    5, 11,  7, 11, 13,  8,  5,  6, 13, 11, 10, 13,  7,  6, 13, 11,  8,
    8, 10,  6, 10,  9, 12, 15, 11,  9, 15, 11,  7,  8, 11, 10])

Data I/O

Since GAlib is based on NumPy arrays, saving and reading of adjacency matrices, as well as any other output of GAlib functions, can be performed using the usual data I/O functionalities of NumPy. See for example the documentation for functions: loadtxt(), savetxt(), load(), save() and savez(). The tools.py module in pyGAlib provides also some data conversion functionalities.

HOW TO FIND FURTHER DOCUMENTATION

While working in an interactive session, after importing a module, the built-in help() function will show further details:

>>> help(modulename)

The help for galib (help(galib)) shows the general summary of the package and a list of all the modules in the library. The help for each module, e.g., help(galib.metrics) or help(galib.models) will display module specific information and a list of all the functions in the module. For further details regarding each function, type:

>>> help(galib.modulename.functionname)

IPython and Jupyter notebook users, the help command is replaced by a question mark after the module's or function's name, e.g.:

>>> modulename?
>>> functionname?

For questions, bug reports, etc, please write to gorka@Zamora-Lopez.xyz, or open an issue in GitHub.

FUTURE DEVELOPMENTS

See the TODO.md file in the GitHub Repository. **Collaboratio

PyGAlib

Install / Use

README