Name | DeepGraph JSON |

Version | 0.2.1 JSON |

download | |

home_page | https://github.com/deepgraph/deepgraph/ |

Summary | Analyze Data with Pandas-based Networks. |

upload_time | 2018-03-13 16:31:43 |

maintainer | |

docs_url | None |

author | Dominik Traxl |

requires_python | |

license | BSD |

keywords | |

VCS | |

bugtrack_url | |

requirements | No requirements were recorded. |

Travis-CI | No Travis. |

coveralls test coverage | No coveralls. |

|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 graphâ€™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> .. |Anaconda Version| image:: https://anaconda.org/conda-forge/deepgraph/badges/version.svg :target: https://anaconda.org/conda-forge/deepgraph .. |Anaconda Downloads| image:: https://anaconda.org/conda-forge/deepgraph/badges/downloads.svg :target: https://anaconda.org/conda-forge/deepgraph .. |Anaconda Install| image:: https://anaconda.org/conda-forge/deepgraph/badges/installer/conda.svg :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 .. |PyPi| image:: https://badge.fury.io/py/DeepGraph.svg :target: https://badge.fury.io/py/DeepGraph

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It implements a\n`network representation <https://en.wikipedia.org/wiki/Network_theory>`_ based\non `pandas <http://pandas.pydata.org/>`_\n`DataFrames <http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html>`_\nand provides methods to construct, partition and plot networks, to interface\nwith popular network packages and more.\n\nIt is based on a new network representation introduced\n`here <http://arxiv.org/abs/1604.00971>`_. DeepGraph is also capable of\nrepresenting\n`multilayer networks <http://deepgraph.readthedocs.io/en/latest/tutorials/terrorists.html>`_.\n\n\nMain Features\n-------------\n\nThis network package is targeted specifically towards\n`Pandas <http://pandas.pydata.org/>`_ users. Utilizing one of Pandas' primary\ndata structures, the\n`DataFrame <http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html>`_,\nwe represent the (super)nodes of a graph by one set of tables, and their\npairwise relations (i.e. the (super)edges of a graph) by another set of tables.\nDeepGraph's main features are\n\n- `Create edges <https://deepgraph.readthedocs.io/en/latest/api_reference.html#creating-edges>`_:\n Methods that enable an iterative, yet\n vectorized computation of pairwise relations (edges) between nodes using\n arbitrary, user-defined functions on the nodes' properties. The methods\n provide arguments to parallelize the computation and control memory consumption,\n making them suitable for very large data-sets and adjustable to whatever\n hardware you have at hand (from netbooks to cluster architectures).\n\n- `Partition nodes, edges or a graph <https://deepgraph.readthedocs.io/en/latest/api_reference.html#graph-partitioning>`_:\n Methods to partition nodes,\n edges or a graph by the graph\u2019s properties and labels, enabling the\n aggregation, computation and allocation of information on and between\n arbitrary *groups* of nodes. These methods also let you express\n elaborate queries on the information contained in a deep graph.\n\n- `Interfaces to other packages <https://deepgraph.readthedocs.io/en/latest/api_reference.html#graph-interfaces>`_:\n Methods to convert to common\n network representations and graph objects of popular Python network packages\n (e.g., SciPy sparse matrices, NetworkX graphs, graph-tool graphs).\n\n- `Plotting <https://deepgraph.readthedocs.io/en/latest/api_reference.html#plotting-methods>`_:\n A number of useful plotting methods for networks,\n including drawings on geographical map projections.\n\n\nQuick Start\n-----------\n\nDeepGraph can be installed via pip from\n`PyPI <https://pypi.python.org/pypi/deepgraph>`_\n\n::\n\n $ pip install deepgraph\n\nor if you're using `Conda <http://conda.pydata.org/docs/>`_,\ninstall with\n\n::\n\n $ conda install -c conda-forge deepgraph\n\nThen, import and get started with::\n\n >>> import deepgraph as dg\n >>> help(dg)\n\n\nDocumentation\n-------------\n\nThe official documentation is hosted here:\nhttp://deepgraph.readthedocs.io\n\nThe documentation provides a good starting point for learning how\nto use the library. 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