deepgraph

Analyze Data with Pandas-based Networks. Documentation:

https://github.com/deepgraph/deepgraph

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Analyze Data with Pandas-based Networks. Documentation:

README.rst

|PyPi Version| |PyPi Downloads| |Conda Version| |Conda Downloads| |Documentation|

DeepGraph
=========

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 networks, to interface
with popular network packages and more.

It is based on the network representation introduced
`here `_. DeepGraph is also capable of
representing
`multilayer networks `_.


Main Features
-------------

Utilizing one of Pandas' primary data structures, the
`DataFrame `_,
DeepGraph represents the (super)nodes of a graph by one (set of) table(s), and their
pairwise relations (i.e. the (super)edges of a graph) by another (set of) table(s).
DeepGraph's main features are

- `Create edges `_:
  Methods that enable an iterative, yet
  vectorized computation of pairwise relations (edges) between nodes using
  arbitrary, user-defined functions on the nodes' properties. The methods
  provide arguments to parallelize the computation and control memory consumption,
  making them suitable for very large data-sets and adjustable to whatever
  hardware you have at hand (from netbooks to cluster architectures).

  Note: the documentation provides a
  `tutorial `_
  on how to compute large correlation matrices in parallel using DeepGraph.

- `Partition nodes, edges or a graph `_:
  Methods to partition nodes,
  edges or a graph by the graph’s properties and labels, enabling the
  aggregation, computation and allocation of information on and between
  arbitrary *groups* of nodes. These methods also let you express
  elaborate queries on the information contained in a deep graph.

- `Interfaces to other packages `_:
  Methods to convert to common
  network representations and graph objects of popular Python network packages
  (e.g., SciPy sparse matrices, NetworkX graphs, graph-tool graphs).

- `Plotting `_:
  A number of useful plotting methods for networks,
  including drawings on geographical map projections using `basemap `__.


Quick Start
-----------

The source code is hosted on GitHub at: https://github.com/deepgraph/deepgraph.

Binary installers are available at the
`Python Package Index (PyPI) `_
and on
`conda-forge `_.

DeepGraph can be installed via pip::

   $ pip install deepgraph

or if you're using `Conda `_,
install with::

   $ conda install -c conda-forge deepgraph

Then, import and get started with::

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

Dependencies
------------

**Required dependencies**

+---------------------------------------+---------------------------+
| Package                               | Minimum supported version |
+=======================================+===========================+
| `Python `_   | 3.9                       |
+---------------------------------------+---------------------------+
| `NumPy `_      | 1.21.6                    |
+---------------------------------------+---------------------------+
| `Pandas `_ | 1.2                       |
+---------------------------------------+---------------------------+

**Optional dependencies ("extras")**

+-----------------------------------------------------+-----------------+-----------+
| Dependency                                          | Minimum Version | pip extra |
+=====================================================+=================+===========+
| `Matplotlib `_              | 3.1             | plot      |
+-----------------------------------------------------+-----------------+-----------+
| `basemap `_ | 2.0             | basemap   |
+-----------------------------------------------------+-----------------+-----------+
| `PyTables `_              | 3.7             | tables    |
+-----------------------------------------------------+-----------------+-----------+
| `SciPy `_                    | 1.5.4           | scipy     |
+-----------------------------------------------------+-----------------+-----------+
| `NetworkX `_           | 2.4             | networkx  |
+-----------------------------------------------------+-----------------+-----------+
| `graph\_tool `_      | 2.27            | N/A       |
+-----------------------------------------------------+-----------------+-----------+

See the `full installation instructions `_
for further details.


Documentation
-------------

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

The documentation provides a good starting point for learning how
to use the library.

The `API Reference `_
lists all available methods of the core
`DeepGraph `_
class, including links to their respective source code and docstrings. These docstrings
provide detailed information, usage examples and notes for each method.


Development
-----------

All forms of contributions to this project are welcome, whether it's bug reports, bug fixes,
documentation enhancements, feature requests, or new ideas.

How to Contribute

- Report Issues: If you encounter any bugs or issues, please
  `create an issue `_ detailing the problem.
- Submit Pull Requests: For bug fixes, enhancements, or new features, fork the repository and
  submit a pull request with your changes.
- Documentation Improvements: Help us improve our documentation by suggesting edits or additions.
- Share Ideas: Have an idea to improve the project? Feel free to
  `open a discussion `_.

For additional inquiries or direct communication, you can reach me via email: dominik.traxl@posteo.org.


How to Get Started as a Developer
---------------------------------

See the `Installation from Source & Environment Setup
`_
section in the documentation for complete instructions on building from the git source tree.


Citing DeepGraph
----------------

Please acknowledge the authors and cite the use of this software when results
are used in publications or published elsewhere. Various citation formats are
available here:
https://dx.doi.org/10.1063/1.4952963
For your convenience, you can find the BibTex entry below:

::

   @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}
   }


Licence
-------

Distributed with a `BSD-3-Clause License. `_::

    Copyright (C) 2017-2025 DeepGraph Developers
    Dominik Traxl 


.. |PyPi Version| image:: https://badge.fury.io/py/DeepGraph.svg
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.. |PyPi Downloads| image:: https://static.pepy.tech/badge/deepgraph/month
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.. |Conda Version| image:: https://anaconda.org/conda-forge/deepgraph/badges/version.svg
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.. |Conda Downloads| image:: https://img.shields.io/conda/dn/conda-forge/deepgraph.svg?label=Conda%20downloads
   :target: https://anaconda.org/conda-forge/deepgraph

.. |Documentation| image:: https://readthedocs.org/projects/deepgraph/badge/?version=latest
    :target: http://deepgraph.readthedocs.io/en/latest/?badge=latest

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