Random Graph Generator

Overview

pyrgg is an easy to use synthetic random graph generator written in python, that support variant graph file formats include DIMACS(.gr) pyrgg has the ability to generate graphs in different sizes and designed to provide input files for test, benchmark and performance-analysis of graph processing frameworks, but it can be used in any other research program that include graphs with these formats.

Installation

Source Code

Download Version 0.1 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

Check Python Packaging User Guide
pip install pyrgg or pip3 install pyrgg (Need root access)

Usage

Download Full Run Video (2.8 MB - MKV)( This video recorded by simplescreenrecorder )

Test

python3 -m pyrgg test or python -m pyrgg test

This test run automatically in each commit ;-)

Linux	Windows

Issues & Bug Reports

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

TODO

Sample Files

Sample 1-DIMACS (100 Vertices , 3KB)
Sample 2-DIMACS (1000 Vertices , 13KB)
Sample 3-DIMACS (1000000 Vertices , 7MB)
Sample 4-DIMACS (5000000 Vertices , 37MB)
Sample 1-JSON (100 Vertices , 11KB)
Sample 2-JSON (1000 Vertices , 105KB)
Sample 1-CSV (100 Vertices , 3KB)
Sample 2-CSV (1000 Vertices , 51KB)
Sample 1-WEL (100 Vertices , 5KB)
Sample 2-WEL (1000 Vertices , 192KB)
Sample 1-YAML (30 Vertices , 3KB)
Sample 2-YAML (100 Vertices , 12KB)
Sample 1-LP (100 Vertices , 7KB)
Sample 2-LP (1000 Vertices , 76KB)
Sample 1-Pickle (100 Vertices , 15KB)
Sample 2-Pickle (1000 Vertices , 209KB)
Sample 1-TGF (100 Vertices , 4KB)
Sample 2-TGF (1000 Vertices , 61KB)
Sample 1-UCINET DL (100 Vertices , 8KB)
Sample 2-UCINET DL (1000 Vertices , 729KB)

Example Of Usage

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

Example. Performance comparison between Medusa and Basic implementation (RMAT&Random are synthetic)

Generate synthetic data for graph benchmark suite like GAP

Supported Formats

DIMACS(.gr)

 	p sp <number of vertices> <number of directed edge>
 	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>

JSON(.json)

 	{
 	"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'

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

Pickle(.p) (Binary Format)

Contribution

You can fork the repository, improve or fix some part of it and then send the pull requests back if you want to see them here. I really appreciate that. ❤️

Remember to write a few tests for your code before sending pull requests.

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