DeepGraph is a scalable, general-purpose data analysis package. It implements a network representation based on pandas DataFrames and provides methods to construct, partition and plot graphs, to interface with popular network packages and more.

It is based on a new network representation introduced here. DeepGraph is also capable of representing multilayer networks.

DeepGraph can be installed via pip from PyPI

$ pip install deepgraph

or if you're using Conda, install with

$ conda install -c https://conda.anaconda.org/deepgraph deepgraph

Then, import and get started with:

>>> import deepgraph as dg
>>> help(dg)

The official documentation is hosted here: http://deepgraph.readthedocs.io

The documentation provides a good starting point for learning how to use the library. Expect the docs to continue to expand as time goes on.

Since this project is fairly new, it's not unlikely you might encounter some bugs here and there. Although the core functionalities are covered pretty well by test scripts, particularly the plotting methods could use some more testing.

Furthermore, at this point, you can expect rather frequent updates to the package as well as the documentation. So please make sure to check for updates every once in a while.

So far the package has only been developed by me, a fact that I would like to change very much. So if you feel like contributing in any way, shape or form, please feel free to contact me, report bugs, create pull requestes, milestones, etc. You can contact me via email: dominik.traxl@posteo.org

To search for bugs or report them, please use the bug tracker: https://github.com/deepgraph/deepgraph/issues

Please acknowledge and cite the use of this software and its authors when results are used in publications or published elsewhere. You can use the following BibTex entry

@Article{traxl-2016-deep,
    author      = {Dominik Traxl AND Niklas Boers AND J\"urgen Kurths},
    title       = {Deep Graphs - A general framework to represent and analyze
                   heterogeneous complex systems across scales},
    journal     = {Chaos},
    year        = {2016},
    volume      = {26},
    number      = {6},
    eid         = {065303},
    doi         = {http://dx.doi.org/10.1063/1.4952963},
    eprinttype  = {arxiv},
    eprintclass = {physics.data-an, cs.SI, physics.ao-ph, physics.soc-ph},
    eprint      = {http://arxiv.org/abs/1604.00971v1},
    version     = {1},
    date        = {2016-04-04},
    url         = {http://arxiv.org/abs/1604.00971v1}
}

Distributed with a BSD license:

Copyright (C) 2016 DeepGraph Developers
Dominik Traxl <dominik.traxl@posteo.org>