Package Health Score

72 / 100

security
No known security issues
popularity
Limited
maintenance
Healthy
community
Sustainable

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Popularity

Limited

GitHub Stars: 205
Forks: 31
Contributors: 8

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package pyrgg, we found that it has been starred 205 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Nov 13, 2024
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

Yes

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Maintenance

Healthy

Commit Frequency

Open Issues: 3
Open PR: 0
Last Release: 1 month ago
Last Commit: 1 month ago

Further analysis of the maintenance status of pyrgg based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Healthy.

We found that pyrgg demonstrates a positive version release cadence with at least one new version released in the past 3 months.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Sustainable

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 8
Funding: Yes

This project has seen only 10 or less contributors.

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Package

Python Versions Compatibility: >=3.6

Age: 7 years
Latest Release: 1 month ago
Dependencies: 0 Direct
Versions: 16
Maintainers: 0
Wheels: OS Independent

Readme

PyRGG: Python Random Graph Generator

Overview

PyRGG is a user-friendly synthetic random graph generator that is written in Python and supports multiple graph file formats, such as DIMACS-Graph files. It can generate graphs of various sizes and is specifically designed to create input files for a wide range of graph-based research applications, including testing, benchmarking, and performance analysis of graph processing frameworks. PyRGG is aimed at computer scientists who are studying graph algorithms and graph processing frameworks.

Open Hub
PyPI Counter
Github Stars

Branch	master	dev
CI

Code Quality

Installation

PyPI

Check Python Packaging User Guide
pip install pyrgg==1.6

Source Code

Download Version 1.6 or Latest Source
pip install .

Conda

Check Conda Managing Package
conda install -c sepandhaghighi pyrgg

Exe Version

⚠️ Only Windows

⚠️ For PyRGG targeting Windows < 10, the user needs to take special care to include the Visual C++ run-time .dlls(for more information visit here)

Download Exe-Version 1.6
Run PYRGG-1.6.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 (Linux)
Run pyrgg or python -m pyrgg (or run PYRGG.exe)
Enter data

Engines

PyRGG

Parameter	Description
Vertices Number	The total number of vertices in the graph
Min Edge Number	The minimum number of edges connected to each vertex
Max Edge Number	The maximum number of edges connected to each vertex
Weighted / Unweighted	Specifies whether the graph is weighted or unweighted
Min Weight	The minimum weight of the edges (if weighted)
Max Weight	The maximum weight of the edges (if weighted)
Signed / Unsigned	Specifies whether the edge weights are signed or unsigned
Directed / Undirected	Specifies whether the graph is directed or undirected
Self Loop / No Self Loop	Specifies whether self-loop is allowed or not
Simple / Multigraph	Specifies whether the graph is a simple graph or a multigraph

Erdős–Rényi-Gilbert

Parameter	Description
Vertices Number	The total number of vertices in the graph
Probability	The probability for edge creation between any two vertices
Directed / Undirected	Specifies whether the graph is directed or undirected

Erdős–Rényi

Parameter	Description
Vertices Number	The total number of vertices in the graph
Edge Number	The total number of edges in the graph
Directed / Undirected	Specifies whether the graph is directed or undirected

Supported Formats

DIMACS

	p sp  
	a   

	.
	.
	.
		
	a

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

CSV

	,,

	.
	.
	.
		
	,,

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

TSV

			

	.
	.
	.

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

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
		}
		]
	}
}

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

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

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

Weighted Edge List

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

ASP

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

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

Trivial Graph Format

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

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

UCINET DL Format

	dl
	format=edgelist1
	n=
	data:
	1 2 weight_1
	.
	.
	.
	n k weight_n

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

Matrix Market

    %%MatrixMarket matrix coordinate real general
        
             
    .
    .
    .

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

Graph Line

	 : :  ... :
	 : :  ... :
	.
	.
	.
	 : :  ... :

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

GDF

    nodedef&gt;name VARCHAR,label VARCHAR
    node_1,node_1_label
    node_2,node_2_label
    .
    .
    .
    node_n,node_n_label
    edgedef&gt;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

    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
      ]
    ]

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

GEXF

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

Graphviz

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

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

Pickle

⚠️ Binary format

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

Issues & Bug Reports

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

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.

14- Gilbert, Edgar N. "Random graphs." The Annals of Mathematical Statistics 30.4 (1959): 1141-1144.

15- Erdős, Paul, and Alfréd Rényi. "On the strength of connectedness of a random graph." Acta Mathematica Hungarica 12.1 (1961): 261-267.

Show Your Support

Star This Repo

Give a ⭐️ if this project helped you!

Donate to Our Project

If you do like our project and we hope that you do, can you please support us? Our project is not and is never going to be working for profit. We need the money just so we can continue doing what we do 😉 .

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

1.6 - 2024-11-13

Added

pyrgg.engines.erdos_reyni module
save_log function

Changed

PyPI badge in README.md updated
logger function format for erdos_reyni_gilbert changed
GitHub actions are limited to the dev and master branches
README.md modified
build_exe.bat modified
Python 3.13 added to test.yml

1.5 - 2024-09-16

Added

feature_request.yml template
config.yml for issue template
pyrgg.engines package
pyrgg.engines.pyrgg module
pyrgg.engines.erdos_reyni_gilbert module
Erdős-Rényi-Gilbert generation model
Generation engine menu
handle_string function
handle_pos_int function
handle_output_format function
handle_engine function
SECURITY.md

Changed

Metadata in files modified
Python 3.5 support dropped
Bug report template modified
Cprofile tests separated in files for engines
README.md modified
Python 3.12 added to test.yml
Menu options bug fixed
Test system modified
engine parameter added to logger function
MENU_ITEMS1 parameter changed to MENU_ITEMS
MENU_ITEMS2 parameter changed to PYRGG_ENGINE_PARAMS
_update_using_first_menu function changed to _update_using_menu
_update_using_second_menu function changed to _update_with_engine_params
ITEM_CONVERTORS renamed to ITEM_HANDLERS
Website domain changed to https://www.pyrgg.site

Removed

dimacs_init function

1.4 - 2023-07-06

Added

check_for_config function
load_config function
save_config function

Changed

README.md modified
Logo changed
codecov removed from dev-requirements.txt
Test system modified
Error messages updated

1.3 - 2022-11-30

Added

Graphviz(DOT) format

Changed

asciinema instruction video updated
Test system modified
README.md modified
Python 3.11 added to test.yml
CLI mode updated
dev-requirements.txt updated
To-do list moved to TODO.md

1.2 - 2022-09-07

Added

Anaconda workflow
Discord badge

Changed

Menu optimized
Docstrings modified
branch_gen function modified
edge_gen function modified
precision and min_edge parameters added to branch_gen function
random_edge parameter removed from branch_gen function
Test system modified
AUTHORS.md updated
License updated
README.md modified
Python 3.10 added to test.yml

Removed

sign_gen function
random_edge_limits function

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

FAQs

What is pyrgg?

Python Random Graph Generator. Visit Snyk Advisor to see a full health score report for pyrgg, including popularity, security, maintenance & community analysis.

Is pyrgg popular?

The python package pyrgg receives a total of 136 weekly downloads. As such, pyrgg popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is pyrgg well maintained?

We found that pyrgg demonstrated a healthy version release cadence and project activity. It has a community of 8 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is pyrgg safe to use?

The python package pyrgg was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 25 December-2024, at 00:28 (UTC).

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