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Acture/Easy-Graph

 
 

EasyGraph

Copyright (C) <2020-2022> by Mobile Systems and Networking Group, Fudan University

https://img.shields.io/pypi/l/Python-EasyGraph

Introduction

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

Install

Installation with pip

$ pip install Python-EasyGraph

or conda

$ conda install Python-EasyGraph

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}
}

About

EasyGraph is an open-source network analysis library designed to cover advanced network processing methods. It includes functionalities for detecting structural hole spanners, network embedding, and various classic network analysis techniques.

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License

BSD-3-Clause, Unknown licenses found

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BSD-3-Clause
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Unknown
LICENSE.NetworkX

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  • Python 92.7%
  • C++ 5.8%
  • Other 1.5%