Python Random Graph Generator

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Project description

Random Graph Generator

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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Branch	master	dev
CI

Code Quality

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

Check Python Packaging User Guide
pip install pyrgg==1.1 or pip3 install pyrgg==1.1 (Need root access)

Conda

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

Exe Version (Only Windows)

Download Exe-Version 1.1
Run PYRGG-1.1.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

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

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 , 26KB)
Sample 2-JSON (1000 Vertices , 494KB)
Sample 1-CSV (100 Vertices , 3KB)
Sample 2-CSV (1000 Vertices , 51KB)
Sample 1-TSV (100 Vertices , 29KB)
Sample 2-TSV (1000 Vertices , 420KB)
Sample 1-WEL (100 Vertices , 5KB)
Sample 2-WEL (1000 Vertices , 192KB)
Sample 1-YAML (30 Vertices , 6KB)
Sample 2-YAML (100 Vertices , 35KB)
Sample 1-LP (100 Vertices , 7KB)
Sample 2-LP (1000 Vertices , 76KB)
Sample 1-Pickle (100 Vertices , 12KB)
Sample 2-Pickle (1000 Vertices , 340KB)
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)
Sample 1-MTX (100 Vertices , 59KB)
Sample 2-MTX (1000 Vertices , 1.8MB)
Sample 1-GL (100 Vertices , 17KB)
Sample 2-GL (1000 Vertices , 2.4MB)
Sample 1-GDF (100 Vertices , 21KB)
Sample 2-GDF (1000 Vertices , 690KB)
Sample 1-GML (100 Vertices , 120KB)
Sample 2-GML (1000 Vertices , 2.4MB)
Sample 1-GEXF (100 Vertices , 63KB)
Sample 2-GEXF (1000 Vertices , 6.4MB)

Example of Usage

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

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++)

Dependencies

master	dev

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

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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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

Unreleased

1.1 - 2021-06-09

Added

requirements-splitter.py
is_weighted function
_write_properties_to_json function
PYRGG_TEST_MODE parameter

Changed

Test system modified
JSON, YAML and Pickle formats value changed from string to number
properties section added to JSON, YAML and Pickle formats
_write_to_json function renamed to _write_data_to_json
logger function modified
time_convert function modified
branch_gen function modified
References updated

1.0 - 2021-01-11

Added

Number of files option

Changed

All flags type changed to bool
Menu optimized
The logger function enhanced.
Time format in the logger changed to %Y-%m-%d %H:%M:%S
dl_maker function modified
tgf_maker function modified
gdf_maker function modified
run function modified

0.9 - 2020-10-07

Added

GEXF format
Float weight support
tox.ini

Changed

Menu optimized
pyrgg.py renamed to graph_gen.py
Other functions moved to functions.py
Test system modified
params.py refactored
graph_gen.py refactored
functions.py refactored
weight_str_to_number function renamed to convert_str_to_number
branch_gen function bugs fixed
input_filter function bug fixed
gl_maker function bug fixed
CONTRIBUTING.md updated
AUTHORS.md updated

Removed

print_test function
left_justify function
justify function
zero_insert function

0.8 - 2020-08-19

Added

GDF format
GML format

Changed

CLI snapshots updated
AUTHORS.md updated

0.7 - 2020-08-07

Added

Graph Line format

Changed

Menu optimized

0.6 - 2020-07-24

Added

Matrix Market format

Changed

json_maker function optimized
dl_maker function optimized
tgf_maker function optimized
lp_maker function optimized

0.5 - 2020-07-01

Added

TSV format
Multigraph control

Changed

branch_gen function modified
Website changed to https://www.pyrgg.ir

0.4 - 2020-06-17

Added

Self loop control
Github action

Changed

appveyor.yml updated

0.3 - 2019-11-29

Added

__version__ variable
CHANGELOG.md
dev-requirements.txt
requirements.txt
CODE_OF_CONDUCT.md
ISSUE_TEMPLATE.md
PULL_REQUEST_TEMPLATE.md
CONTRIBUTING.md
version_check.py
pyrgg_profile.py
Unweighted graph
Undirected graph
Exe version

Changed

Test system modified
README.md modified
Docstrings modified
get_input function modified
edge_gen function modified
Parameters moved to params.py

0.2 - 2017-09-20

Added

CSV format
YAML format
Weighted edge list format (WEL)
ASP format
Trivial graph format (TGF)
UCINET DL format
Pickle format

0.1 - 2017-08-19

Added

DIMACS format
JSON format
README

Project details

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

1.5

Sep 16, 2024

1.4

Jul 6, 2023

1.3

Nov 30, 2022

1.2

Sep 7, 2022

This version

1.1

Jun 9, 2021

1.0

Jan 11, 2021

0.9

Oct 7, 2020

0.8

Aug 19, 2020

0.7

Aug 7, 2020

0.6

Jul 24, 2020

0.5

Jul 1, 2020

0.4

Jun 17, 2020

0.3

Nov 29, 2019

0.2

Sep 20, 2017

0.1

Jul 13, 2017

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pyrgg 1.1

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Project description

Random Graph Generator

Table of Contents

Overview

Installation

Source Code

PyPI

Conda

Exe Version (Only Windows)

System Requirements

Usage

Issues & Bug Reports

TODO

Sample Files

Example of Usage

Supported Formats

Similar Works

Dependencies

Citing

License

References

Show Your Support

Star This Repo

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Changelog

Unreleased

1.1 - 2021-06-09

Added

Changed

1.0 - 2021-01-11

Added

Changed

0.9 - 2020-10-07

Added

Changed

Removed

0.8 - 2020-08-19

Added

Changed

0.7 - 2020-08-07

Added

Changed

0.6 - 2020-07-24

Added

Changed

0.5 - 2020-07-01

Added

Changed

0.4 - 2020-06-17

Added

Changed

0.3 - 2019-11-29

Added

Changed

0.2 - 2017-09-20

Added

0.1 - 2017-08-19

Added

Project details

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