pyr

pyrgg

🔧 Python Random Graph Generator

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Random Graph Generator

PyPI version Codecov built with Python3


Table of Contents

Overview

Pyrgg is an easy-to-use synthetic random graph generator written in Python which supports various graph file formats including DIMACS .gr files. Pyrgg has the ability to generate graphs of different sizes and is designed to provide input files for broad range of graph-based research applications, including but not limited to testing, benchmarking and performance-analysis of graph processing frameworks. Pyrgg target audiences are computer scientists who study graph algorithms and graph processing frameworks.

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Installation

Source Code

  • Download Version 1.1 or Latest Source
  • pip install -r requirements.txt or pip3 install -r requirements.txt (Need root access)
  • python3 setup.py install or python setup.py install (Need root access)

PyPI

Conda

Exe Version (Only Windows)

System Requirements

Pyrgg will likely run on a modern dual core PC. Typical configuration is:

  • Dual Core CPU (2.0 Ghz+)
  • 4GB of RAM

Note that it may run on lower end equipment though good performance is not guaranteed.

Usage

Issues & Bug Reports

Just fill an issue and describe it. I'll check it ASAP! or send an email to info@pyrgg.ir.

TODO

  • Formats
    • DIMACS
    • JSON
    • YAML
    • Pickle
    • CSV
    • TSV
    • WEL
    • ASP
    • TGF
    • UCINET DL
    • GML
    • GDF
    • Matrix Market
    • Graph Line
    • GEXF
  • Sizes
    • Small
    • Medium
    • Large
  • Weighted Graph
    • Signed Weights
  • Unweighted Graph
  • Dense Graph
  • Sparse Graph
  • Directed Graph
  • Self loop
  • Parallel Arc
  • Multithreading
  • GUI
  • Erdős–Rényi model
  • Tree

Sample Files

Example of Usage

  • Generate synthetic data for graph processing frameworks (some of them mentioned here) performance-analysis

    - [Medusa](https://github.com/JianlongZhong/Medusa "Medusa") 
    - [Totem](https://github.com/netsyslab/Totem "Totem")
    - [Frog](https://github.com/AndrewStallman/Frog "Frog")
    - [CuSha](https://github.com/farkhor/CuSha "CuSha")
    

    Fig. 1. Rand Graph Generation

  • Generate synthetic data for graph benchmark suite like GAP

Supported Formats

  • DIMACS(.gr)

        p sp <number of vertices> <number of edges>
        a <head_1> <tail_1> <weight_1>
    
        .
        .
        .
        
        a <head_n> <tail_n> <weight_n>
    
  • CSV(.csv)

        <head_1>,<tail_1>,<weight_1>
    
        .
        .
        .
        
        <head_n>,<tail_n>,<weight_n>
    
  • TSV(.tsv)

        <head_1>    <tail_1>    <weight_1>
    
        .
        .
        .
        
        <head_n>    <tail_n>    <weight_n>
    
  • JSON(.json)

    {
        "properties": {
            "directed": true,
            "signed": true,
            "multigraph": true,
            "weighted": true,
            "self_loop": true
        },
        "graph": {
            "nodes":[
            {
                "id": 1
            },
    
            .
            .
            .
    
            {
                "id": n
            }
            ],
            "edges":[
            {
                "source": head_1,
                "target": tail_1,
                "weight": weight_1
            },
    
            .
            .
            .
    
            {
                "source": head_n,
                "target": tail_n,
                "weight": weight_n
            }
            ]
        }
    }
    
  • YAML(.yaml)

        graph:
                edges:
            - source: head_1
            target: tail_1
            weight: weight_1
        
            .
            .
            .
    
            - source: head_n
            target: tail_n
            weight: weight_n
                        
            nodes:
                - id: 1
    
                .
            .
            .
    
            - id: n
        properties:
                directed: true
                multigraph: true
                self_loop: true
                signed: true
                weighted: true
    
    
  • Weighted Edge List(.wel)

        <head_1> <tail_1> <weight_1>
        
        .
        .
        .
        
        <head_n> <tail_n> <weight_n>    
    
  • ASP(.lp)

        node(1).
        .
        .
        .
        node(n).
        edge(head_1,tail_1,weight_1).
        .
        .
        .
        edge(head_n,tail_n,weight_n).
    
  • Trivial_Graph_Format(.tgf)

        1
        .
        .
        .
        n
        #
        1 2 weight_1
        .
        .
        .
        n k weight_n
    
  • UCINET DL Format(.dl)

        dl
        format=edgelist1
        n=<number of vertices>
        data:
        1 2 weight_1
        .
        .
        .
        n k weight_n    
    
  • Matrix Market(.mtx)

      %%MatrixMarket matrix coordinate real general
        <number of vertices>  <number of vertices>  <number of edges>
        <head_1>    <tail_1>    <weight_1> 
        .
        .
        .
        <head_n>    <tail_n>    <weight_n> 
    
  • Graph Line(.gl)

       <head_1> <tail_1>:<weight_1> <tail_2>:<weight_2>  ... <tail_n>:<weight_n>
       <head_2> <tail_1>:<weight_1> <tail_2>:<weight_2>  ... <tail_n>:<weight_n>
       .
       .
       .
       <head_n> <tail_1>:<weight_1> <tail_2>:<weight_2>  ... <tail_n>:<weight_n>
    
  • GDF(.gdf)

       nodedef>name VARCHAR,label VARCHAR
         node_1,node_1_label
         node_2,node_2_label
         .
         .
         .
         node_n,node_n_label
         edgedef>node1 VARCHAR,node2 VARCHAR, weight DOUBLE
         node_1,node_2,weight_1
         node_1,node_3,weight_2
         .
         .
         .
         node_n,node_2,weight_n 
    
  • GML(.gml)

         graph
       [
           multigraph 0
           directed  0
           node
           [
            id 1
            label "Node 1"
           ]
           node
           [
            id 2
            label "Node 2"
           ]
           .
           .
           .
           node
           [
            id n
            label "Node n"
           ]
           edge
           [
            source 1
            target 2
            value W1
           ]
           edge
           [
            source 2
            target 4
            value W2
           ]
           .
           .
           .
           edge
           [
            source n
            target r
            value Wn
           ]
         ]
    
  • GEXF(.gexf)

        <?xml version="1.0" encoding="UTF-8"?>
        <gexf xmlns="http://www.gexf.net/1.2draft" version="1.2">
            <meta lastmodifieddate="2009-03-20">
                <creator>PyRGG</creator>
                <description>File Name</description>
            </meta>
            <graph defaultedgetype="directed">
                <nodes>
                    <node id="1" label="Node 1" />
                    <node id="2" label="Node 2" />
                    ...
                </nodes>
                <edges>
                    <edge id="1" source="1" target="2" weight="400" />
                    ...
                </edges>
            </graph>
        </gexf>
    
  • Pickle(.p) (Binary Format)

Similar Works

Dependencies

master dev
Requirements Status Requirements Status

Citing

If you use pyrgg in your research, please cite the JOSS paper ;-)

@article{Haghighi2017,
  doi = {10.21105/joss.00331},
  url = {https://doi.org/10.21105/joss.00331},
  year  = {2017},
  month = {sep},
  publisher = {The Open Journal},
  volume = {2},
  number = {17},
  author = {Sepand Haghighi},
  title = {Pyrgg: Python Random Graph Generator},
  journal = {The Journal of Open Source Software}
}
JOSS
Zenodo DOI

License

References

1- 9th DIMACS Implementation Challenge - Shortest Paths
2- Problem Based Benchmark Suite
3- MaximalClique - ASP Competition 2013
4- Pitas, Ioannis, ed. Graph-based social media analysis. Vol. 39. CRC Press, 2016.
5- Roughan, Matthew, and Jonathan Tuke. "The hitchhikers guide to sharing graph data." 2015 3rd International Conference on Future Internet of Things and Cloud. IEEE, 2015.
6- Borgatti, Stephen P., Martin G. Everett, and Linton C. Freeman. "Ucinet for Windows: Software for social network analysis." Harvard, MA: analytic technologies 6 (2002).
7- Matrix Market: File Formats
8- Social Network Visualizer
9- Adar, Eytan. "GUESS: a language and interface for graph exploration." Proceedings of the SIGCHI conference on Human Factors in computing systems. 2006.
10- Skiena, Steven S. The algorithm design manual. Springer International Publishing, 2020.
11- Chakrabarti, Deepayan, Yiping Zhan, and Christos Faloutsos. "R-MAT: A recursive model for graph mining." Proceedings of the 2004 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2004.
12- Zhong, Jianlong, and Bingsheng He. "An overview of medusa: simplified graph processing on gpus." ACM SIGPLAN Notices 47.8 (2012): 283-284.
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