EasyGraph

Copyright (C) <2020-2024> by DataNET Group, Fudan University

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• Documentation: https://easy-graph.github.io/
• Source Code: https://github.com/easy-graph/Easy-Graph
• Issue Tracker: https://github.com/easy-graph/Easy-Graph/issues
• PyPI Homepage: https://pypi.org/project/Python-EasyGraph/
• Youtube channel: https://www.youtube.com/@python-easygraph

Introduction

EasyGraph is an open-source network analysis library. It is mainly written in Python and supports analysis for undirected networks and directed networks. EasyGraph supports various formats of network data and covers a series of important network analysis algorithms for community detection, structural hole spanner detection, network embedding, and motif detection. Moreover, EasyGraph implements some key elements using C++ and introduces multiprocessing optimization to achieve better efficiency.

New Features in Version 1.1

• Support for more hypergraph metrics and algorithms. Such as hypercoreness, vector-centrality, s-centrality, and so on.
• Support for more hypergraph datasets. Static hypergraph datasets and dynamic datasets can be both loaded by calling the corresponding dataset name.
• Support for more flexible dynamic hypergraph visualization. Users can define dynamic hypergraphs and visualize the structure of the hypergraph at each timestamp.
• Support for more efficient hypergraph computation and hypergraph learning. Adoption of suitable storage structure and caching strategy for different metrics/hypergraph neural networks.

If you need more details, please see our documentation of the latest version.

News

• [04-09-2024] We released EasyGraph 1.2! This version now fully supports Python 3.12.
• [03-06-2024] We received the Shanghai Open Source Innovation Outstanding Achievement Award (Grand Prize)! News
• [02-05-2024] We released EasyGraph 1.1! This version features hypergraph analysis and learning for higher-order network modeling and representation.
• [08-17-2023] We released EasyGraph 1.0!
• [08-08-2023] Our paper "EasyGraph: A Multifunctional, Cross-Platform, and Effective Library for Interdisciplinary Network Analysis" was accepted by Patterns (Cell Press)!

Stargazers

Install

Supported Versions

3.8 <= Python <= 3.12 is required.

Installation With pip

    $ pip install --upgrade Python-EasyGraph

The conda package is no longer updated or maintained.

If you've installed EasyGraph this way before, please uninstall it with conda and install it with pip.

Build From Source

If prebuilt EasyGraph wheels are not supported for your platform (OS / CPU arch, check here), or you want to have GPU-based functions enabled, you can build it locally.

Prerequisites

• CMake >= 3.23
• A compiler that fully supports C++11
• CUDA Toolkit 11.8 or later would be preferred (If need GPUs enabled)

Installation

On Linux

    git clone --recursive https://github.com/easy-graph/Easy-Graph
    export EASYGRAPH_ENABLE_GPU="TRUE"  # for users who want to enable GPUs
    pip install ./Easy-Graph

On Windows

    % For Windows users who want to enable GPU-based functions, %
    % you must execute the commands below in cmd but not PowerShell. %
    git clone --recursive https://github.com/easy-graph/Easy-Graph
    set EASYGRAPH_ENABLE_GPU=TRUE   % for users who want to enable GPUs %
    pip install ./Easy-Graph

On macOS

    # Since macOS doesn't support CUDA, we can't have GPUs enabled on macOS
    git clone --recursive https://github.com/easy-graph/Easy-Graph
    pip install ./Easy-Graph

Hint

EasyGraph uses 1.12.1 <= PyTorch < 2.0 for machine learning functions. Note that this does not prevent your from running non-machine learning functions normally, if there is no PyTorch in your environment. But you will receive some warnings which remind you some unavailable modules when they depend on it.

Simple Example

This example shows the general usage of methods in EasyGraph.

  >>> import easygraph as eg
  >>> G = eg.Graph()
  >>> G.add_edges([(1,2), (2,3), (1,3), (3,4), (4,5), (3,5), (5,6)])
  >>> eg.pagerank(G)
  {1: 0.14272233049003707, 2: 0.14272233049003694, 3: 0.2685427766200994, 4: 0.14336430577918527, 5: 0.21634929087322705, 6: 0.0862989657474143}

This is a simple example for the detection of structural hole spanners using the HIS algorithm.

  >>> import easygraph as eg
  >>> G = eg.Graph()
  >>> G.add_edges([(1,2), (2,3), (1,3), (3,4), (4,5), (3,5), (5,6)])
  >>> _, _, H = eg.get_structural_holes_HIS(G, C=[frozenset([1,2,3]), frozenset([4,5,6])])
  >>> H # The structural hole score of each node. Note that node `4` is regarded as the most possible structural hole spanner.
  {1: {0: 0.703948974609375},
   2: {0: 0.703948974609375},
   3: {0: 1.2799804687499998},
   4: {0: 1.519976806640625},
   5: {0: 1.519976806640625},
   6: {0: 0.83595703125}
  }

Citation

If you use EasyGraph in a scientific publication, we would appreciate citations to the following paper:

  @article{gao2023easygraph,
      title={{EasyGraph: A Multifunctional, Cross-Platform, and Effective Library for Interdisciplinary Network Analysis}},
      author={Min Gao and Zheng Li and Ruichen Li and Chenhao Cui and Xinyuan Chen and Bodian Ye and Yupeng Li and Weiwei Gu and Qingyuan Gong and Xin Wang and Yang Chen},
      year={2023},
      journal={Patterns},
      volume={4},
      number={10},
      pages={100839},
  }