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https://github.com/deepgraph/deepgraph

Analyze Data with Pandas-based Networks. Documentation:
https://github.com/deepgraph/deepgraph

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Analyze Data with Pandas-based Networks. Documentation:

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README 

          

|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

    :target: https://pypi.org/project/DeepGraph/



.. |PyPi Downloads| image:: https://static.pepy.tech/badge/deepgraph/month

   :target: https://pypi.org/project/DeepGraph/



.. |Conda Version| image:: https://anaconda.org/conda-forge/deepgraph/badges/version.svg

   :target: https://anaconda.org/conda-forge/deepgraph



.. |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