PyRGG: Python Random Graph Generator

Table of Contents

• Overview
• Installation
• Usage
• Supported Formats
• Example of Usage
• Similar Works
• Issues & Bug Reports
• Contribution
• References
• Citing
• Authors
• License
• Show Your Support
• Todo
• Changelog
• Code of Conduct

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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Code Quality

Installation

PyPI

• Check Python Packaging User Guide
• pip install pyrgg==1.4 (Need root access)

Source Code

• Download Version 1.4 or Latest Source
• pip install . (Need root access)

Conda

• Check Conda Managing Package
• conda install -c sepandhaghighi pyrgg (Need root access)

Exe Version (Only Windows)

• Download Exe-Version 1.4
• Run PYRGG-1.4.exe

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

• Open CMD (Windows) or Terminal (UNIX)
• Run pyrgg or python -m pyrgg (or run PYRGG.exe)
• Enter data

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>
  

  • Sample 1 (100 Vertices , 3KB)
  • Sample 2 (1000 Vertices , 13KB)
  • Sample 3 (1000000 Vertices , 7MB)
  • Sample 4 (5000000 Vertices , 37MB)

• CSV(.csv)

   	<head_1>,<tail_1>,<weight_1>
  
   	.
   	.
   	.
   	
   	<head_n>,<tail_n>,<weight_n>
  

  • Sample 1 (100 Vertices , 3KB)
  • Sample 2 (1000 Vertices , 51KB)

• TSV(.tsv)

   	<head_1>	<tail_1>	<weight_1>
  
   	.
   	.
   	.
   	
   	<head_n>	<tail_n>	<weight_n>
  

  • Sample 1 (100 Vertices , 29KB)
  • Sample 2 (1000 Vertices , 420KB)

• 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
   		}
   		]
   	}
   }
  

  • Sample 1 (100 Vertices , 26KB)
  • Sample 2 (1000 Vertices , 494KB)

• 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
  
  

  • Sample 1 (30 Vertices , 6KB)
  • Sample 2 (100 Vertices , 35KB)

• Weighted Edge List(.wel)

   	<head_1> <tail_1> <weight_1>
   	
   	.
   	.
   	.
   	
   	<head_n> <tail_n> <weight_n>	
  

  • Sample 1 (100 Vertices , 5KB)
  • Sample 2 (1000 Vertices , 192KB)

• ASP(.lp)

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

  • Sample 1 (100 Vertices , 7KB)
  • Sample 2 (1000 Vertices , 76KB)

• Trivial Graph Format(.tgf)

   	1
   	.
   	.
   	.
   	n
   	#
   	1 2 weight_1
   	.
   	.
   	.
   	n k weight_n
  

  • Sample 1 (100 Vertices , 4KB)
  • Sample 2 (1000 Vertices , 61KB)

• UCINET DL Format(.dl)

   	dl
   	format=edgelist1
   	n=<number of vertices>
   	data:
   	1 2 weight_1
   	.
   	.
   	.
   	n k weight_n	
  

  • Sample 1 (100 Vertices , 8KB)
  • Sample 2 (1000 Vertices , 729KB)

• 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> 
  

  • Sample 1 (100 Vertices , 59KB)
  • Sample 2 (1000 Vertices , 1.8MB)

• 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>
  

  • Sample 1 (100 Vertices , 17KB)
  • Sample 2 (1000 Vertices , 2.4MB)

• 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 
  

  • Sample 1 (100 Vertices , 21KB)
  • Sample 2 (1000 Vertices , 690KB)

• 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
        ]
      ]
  

  • Sample 1 (100 Vertices , 120KB)
  • Sample 2 (1000 Vertices , 2.4MB)

• 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>
  

  • Sample 1 (100 Vertices , 63KB)
  • Sample 2 (1000 Vertices , 6.4MB)

• Graphviz(.gv)

  	graph example 
  		{
  		node1 -- node2 [weight=W1];
  		node3 -- node4 [weight=W2];
  		node1 -- node3 [weight=W3];
  		.
  		.
  		.
  		}
  

  • Sample 1 (100 Vertices , 11KB)
  • Sample 2 (1000 Vertices , 106KB)
  • Online Visualization

• Pickle(.p) (Binary Format)

  • Sample 1 (100 Vertices , 12KB)
  • Sample 2 (1000 Vertices , 340KB)

Example of Usage

• Generate synthetic data for graph processing frameworks (some of them mentioned here) performance-analysis
  • Medusa
  • Totem
  • Frog
  • CuSha
• Generate synthetic data for graph benchmark suite like GAP

Similar Works

• Random Modular Network Generator Generates random graphs with tunable strength of community structure
• randomGraph very simple random graph generator in MATLAB
• Graph1 Random Graph Generator with Max capacity paths (C++)

Issues & Bug Reports

Just fill an issue and describe it. We'll check it ASAP! or send an email to [email protected].

You can also join our discord server

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

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.

13- Ellson, John, et al. "Graphviz and dynagraph—static and dynamic graph drawing tools." Graph drawing software. Springer, Berlin, Heidelberg, 2004. 127-148.

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