Anaconda Version, Anaconda Downloads, Documentation, PyPi, 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 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}
}
Licence
Distributed with a BSD license <LICENSE.txt>_::
Copyright (C) 2017 DeepGraph Developers
Dominik Traxl <dominik.traxl@posteo.org>
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