DeepGraph

Analyze Data with Pandas-based Networks. Documentation:

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DeepGraph

DeepGraph is a scalable, general-purpose data analysis package. It implements a network representation <https://en.wikipedia.org/wiki/Network_theory> based on pandas <http://pandas.pydata.org/> DataFrames <http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html>_ and provides methods to construct, partition and plot networks, to interface with popular network packages and more.

It is based on a new network representation introduced here <http://arxiv.org/abs/1604.00971>. DeepGraph is also capable of representing multilayer networks <http://deepgraph.readthedocs.io/en/latest/tutorials/terrorists.html>.

Main Features

This network package is targeted specifically towards Pandas <http://pandas.pydata.org/> users. Utilizing one of Pandas' primary data structures, the DataFrame <http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html>, we represent the (super)nodes of a graph by one set of tables, and their pairwise relations (i.e. the (super)edges of a graph) by another set of tables. DeepGraph's main features are

  • Create edges <https://deepgraph.readthedocs.io/en/latest/api_reference.html#creating-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).

  • Partition nodes, edges or a graph <https://deepgraph.readthedocs.io/en/latest/api_reference.html#graph-partitioning>_: 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 <https://deepgraph.readthedocs.io/en/latest/api_reference.html#graph-interfaces>_: 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 <https://deepgraph.readthedocs.io/en/latest/api_reference.html#plotting-methods>_: A number of useful plotting methods for networks, including drawings on geographical map projections.

Quick Start

DeepGraph can be installed via pip from PyPI <https://pypi.python.org/pypi/deepgraph>_

::

$ pip install deepgraph

or if you're using Conda <http://conda.pydata.org/docs/>_, install with

::

$ conda install -c conda-forge deepgraph

Then, import and get started with::

import deepgraph as dg help(dg)

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. Expect the docs to continue to expand as time goes on.

Development

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

Bug Reports

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

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://aip.scitation.org/action/showCitFormats?type=show&doi=10.1063%2F1.4952963 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 license <LICENSE.txt>_::

Copyright (C) 2017-2020 DeepGraph Developers
Dominik Traxl <dominik.traxl@posteo.org>

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