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It implements a\n`network representation \u003chttps://en.wikipedia.org/wiki/Network_theory\u003e`_ based\non `pandas \u003chttp://pandas.pydata.org/\u003e`_\n`DataFrames \u003chttps://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html\u003e`_\nand provides methods to construct, partition and plot networks, to interface\nwith popular network packages and more.\n\nIt is based on the network representation introduced\n`here \u003chttp://arxiv.org/abs/1604.00971\u003e`_. DeepGraph is also capable of\nrepresenting\n`multilayer networks \u003chttp://deepgraph.readthedocs.io/en/latest/tutorials/terrorists.html\u003e`_.\n\n\nMain Features\n-------------\n\nUtilizing one of Pandas' primary data structures, the\n`DataFrame \u003chttps://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html\u003e`_,\nDeepGraph represents the (super)nodes of a graph by one (set of) table(s), and their\npairwise relations (i.e. the (super)edges of a graph) by another (set of) table(s).\nDeepGraph's main features are\n\n- `Create edges \u003chttps://deepgraph.readthedocs.io/en/latest/api_reference.html#creating-edges\u003e`_:\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  Note: the documentation provides a\n  `tutorial \u003chttps://deepgraph.readthedocs.io/en/latest/tutorials/pairwise_correlations.html\u003e`_\n  on how to compute large correlation matrices in parallel using DeepGraph.\n\n- `Partition nodes, edges or a graph \u003chttps://deepgraph.readthedocs.io/en/latest/api_reference.html#graph-partitioning\u003e`_:\n  Methods to partition nodes,\n  edges or a graph by the graph’s 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 \u003chttps://deepgraph.readthedocs.io/en/latest/api_reference.html#graph-interfaces\u003e`_:\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 \u003chttps://deepgraph.readthedocs.io/en/latest/api_reference.html#plotting-methods\u003e`_:\n  A number of useful plotting methods for networks,\n  including drawings on geographical map projections using `basemap \u003chttps://github.com/matplotlib/basemap\u003e`__.\n\n\nQuick Start\n-----------\n\nThe source code is hosted on GitHub at: https://github.com/deepgraph/deepgraph.\n\nBinary installers are available at the\n`Python Package Index (PyPI) \u003chttps://pypi.python.org/pypi/deepgraph\u003e`_\nand on\n`conda-forge \u003chttps://anaconda.org/conda-forge/deepgraph\u003e`_.\n\nDeepGraph can be installed via pip::\n\n   $ pip install deepgraph\n\nor if you're using `Conda \u003chttp://conda.pydata.org/docs/\u003e`_,\ninstall with::\n\n   $ conda install -c conda-forge deepgraph\n\nThen, import and get started with::\n\n   \u003e\u003e\u003e import deepgraph as dg\n   \u003e\u003e\u003e help(dg)\n\nDependencies\n------------\n\n**Required dependencies**\n\n+---------------------------------------+---------------------------+\n| Package                               | Minimum supported version |\n+=======================================+===========================+\n| `Python \u003chttps://www.python.org/\u003e`_   | 3.9                       |\n+---------------------------------------+---------------------------+\n| `NumPy \u003chttp://www.numpy.org/\u003e`_      | 1.21.6                    |\n+---------------------------------------+---------------------------+\n| `Pandas \u003chttp://pandas.pydata.org/\u003e`_ | 1.2                       |\n+---------------------------------------+---------------------------+\n\n**Optional dependencies (\"extras\")**\n\n+-----------------------------------------------------+-----------------+-----------+\n| Dependency                                          | Minimum Version | pip extra |\n+=====================================================+=================+===========+\n| `Matplotlib \u003chttp://matplotlib.org/\u003e`_              | 3.1             | plot      |\n+-----------------------------------------------------+-----------------+-----------+\n| `basemap \u003chttps://matplotlib.org/basemap/stable/\u003e`_ | 2.0             | basemap   |\n+-----------------------------------------------------+-----------------+-----------+\n| `PyTables \u003chttp://www.pytables.org/\u003e`_              | 3.7             | tables    |\n+-----------------------------------------------------+-----------------+-----------+\n| `SciPy \u003chttp://www.scipy.org/\u003e`_                    | 1.5.4           | scipy     |\n+-----------------------------------------------------+-----------------+-----------+\n| `NetworkX \u003chttps://networkx.github.io/\u003e`_           | 2.4             | networkx  |\n+-----------------------------------------------------+-----------------+-----------+\n| `graph\\_tool \u003chttps://graph-tool.skewed.de/\u003e`_      | 2.27            | N/A       |\n+-----------------------------------------------------+-----------------+-----------+\n\nSee the `full installation instructions \u003chttps://deepgraph.readthedocs.io/en/latest/installation.html\u003e`_\nfor further details.\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.\n\nThe `API Reference \u003chttps://deepgraph.readthedocs.io/en/latest/api_reference.html\u003e`_\nlists all available methods of the core\n`DeepGraph \u003chttps://deepgraph.readthedocs.io/en/latest/generated/deepgraph.deepgraph.DeepGraph.html\u003e`_\nclass, including links to their respective source code and docstrings. These docstrings\nprovide detailed information, usage examples and notes for each method.\n\n\nDevelopment\n-----------\n\nAll forms of contributions to this project are welcome, whether it's bug reports, bug fixes,\ndocumentation enhancements, feature requests, or new ideas.\n\nHow to Contribute\n\n- Report Issues: If you encounter any bugs or issues, please\n  `create an issue \u003chttps://github.com/deepgraph/deepgraph/issues\u003e`_ detailing the problem.\n- Submit Pull Requests: For bug fixes, enhancements, or new features, fork the repository and\n  submit a pull request with your changes.\n- Documentation Improvements: Help us improve our documentation by suggesting edits or additions.\n- Share Ideas: Have an idea to improve the project? Feel free to\n  `open a discussion \u003chttps://github.com/deepgraph/deepgraph/discussions\u003e`_.\n\nFor additional inquiries or direct communication, you can reach me via email: dominik.traxl@posteo.org.\n\n\nHow to Get Started as a Developer\n---------------------------------\n\nSee the `Installation from Source \u0026 Environment Setup\n\u003chttps://deepgraph.readthedocs.io/en/latest/installation.html#installation-from-source-environment-setup\u003e`_\nsection in the documentation for complete instructions on building from the git source tree.\n\n\nCiting DeepGraph\n----------------\n\nPlease acknowledge the authors and cite the use of this software when results\nare used in publications or published elsewhere. Various citation formats are\navailable here:\nhttps://dx.doi.org/10.1063/1.4952963\nFor your convenience, you can find the BibTex entry below:\n\n::\n\n   @Article{traxl-2016-deep,\n       author      = {Dominik Traxl AND Niklas Boers AND J\\\"urgen Kurths},\n       title       = {Deep Graphs - A general framework to represent and analyze\n                      heterogeneous complex systems across scales},\n       journal     = {Chaos},\n       year        = {2016},\n       volume      = {26},\n       number      = {6},\n       eid         = {065303},\n       doi         = {http://dx.doi.org/10.1063/1.4952963},\n       eprinttype  = {arxiv},\n       eprintclass = {physics.data-an, cs.SI, physics.ao-ph, physics.soc-ph},\n       eprint      = {http://arxiv.org/abs/1604.00971v1},\n       version     = {1},\n       date        = {2016-04-04},\n       url         = {http://arxiv.org/abs/1604.00971v1}\n   }\n\n\nLicence\n-------\n\nDistributed with a `BSD-3-Clause License. \u003chttps://github.com/deepgraph/deepgraph/blob/master/LICENSE\u003e`_::\n\n    Copyright (C) 2017-2025 DeepGraph Developers\n    Dominik Traxl \u003cdominik.traxl@posteo.org\u003e\n\n\n.. |PyPi Version| image:: https://badge.fury.io/py/DeepGraph.svg\n    :target: https://pypi.org/project/DeepGraph/\n\n.. |PyPi Downloads| image:: https://static.pepy.tech/badge/deepgraph/month\n   :target: https://pypi.org/project/DeepGraph/\n\n.. |Conda Version| image:: https://anaconda.org/conda-forge/deepgraph/badges/version.svg\n   :target: https://anaconda.org/conda-forge/deepgraph\n\n.. |Conda Downloads| image:: https://img.shields.io/conda/dn/conda-forge/deepgraph.svg?label=Conda%20downloads\n   :target: https://anaconda.org/conda-forge/deepgraph\n\n.. |Documentation| image:: https://readthedocs.org/projects/deepgraph/badge/?version=latest\n    :target: http://deepgraph.readthedocs.io/en/latest/?badge=latest\n","funding_links":[],"categories":["图数据处理","Python"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepgraph%2Fdeepgraph","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeepgraph%2Fdeepgraph","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepgraph%2Fdeepgraph/lists"}