https://github.com/deepgraph/deepgraph
Analyze Data with Pandas-based Networks. Documentation:
https://github.com/deepgraph/deepgraph
data-analysis data-mining data-science data-structures data-visualization graph-database graph-theory graphs graphviz interfacing iterative-methods multilayer-networks network network-analysis network-visualization networkx pandas parallel partitioning
Last synced: 1 day ago
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Analyze Data with Pandas-based Networks. Documentation:
- Host: GitHub
- URL: https://github.com/deepgraph/deepgraph
- Owner: deepgraph
- License: other
- Created: 2015-10-27T12:28:45.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2024-09-03T22:45:13.000Z (7 months ago)
- Last Synced: 2024-10-01T23:06:07.829Z (6 months ago)
- Topics: data-analysis, data-mining, data-science, data-structures, data-visualization, graph-database, graph-theory, graphs, graphviz, interfacing, iterative-methods, multilayer-networks, network, network-analysis, network-visualization, networkx, pandas, parallel, partitioning
- Language: Python
- Homepage: http://deepgraph.readthedocs.io
- Size: 31.4 MB
- Stars: 284
- Watchers: 19
- Forks: 40
- Open Issues: 11
-
Metadata Files:
- Readme: README.rst
- License: LICENSE.txt
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README
|Anaconda Version| |Anaconda Downloads| |Documentation| |PyPi|
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 a new network representation introduced
`here `_. DeepGraph is also capable of
representing
`multilayer networks `_.
Main Features
-------------
This network package is targeted specifically towards
`Pandas `_ users. Utilizing one of Pandas' primary
data structures, the
`DataFrame `_,
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 `_:
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 `_:
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.
Quick Start
-----------
DeepGraph can be installed via pip from
`PyPI `_
::
$ 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)
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: [email protected]
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 `_::
Copyright (C) 2017-2020 DeepGraph Developers
Dominik Traxl
.. |Anaconda Version| image:: https://anaconda.org/conda-forge/deepgraph/badges/version.svg
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