Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/sepandhaghighi/pyrgg
🔧 Python Random Graph Generator
https://github.com/sepandhaghighi/pyrgg
beerpay generator gpu graph-computing graph-generation graph-generator gtgraph math mathematics python python3 script
Last synced: about 16 hours ago
JSON representation
🔧 Python Random Graph Generator
- Host: GitHub
- URL: https://github.com/sepandhaghighi/pyrgg
- Owner: sepandhaghighi
- License: mit
- Created: 2017-04-25T21:49:53.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-06-11T09:21:44.000Z (6 months ago)
- Last Synced: 2024-06-11T17:43:36.534Z (6 months ago)
- Topics: beerpay, generator, gpu, graph-computing, graph-generation, graph-generator, gtgraph, math, mathematics, python, python3, script
- Language: Python
- Homepage: https://www.pyrgg.site
- Size: 9.6 MB
- Stars: 204
- Watchers: 4
- Forks: 28
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: .github/CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Authors: AUTHORS.md
Awesome Lists containing this project
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](https://packaging.python.org/installing/)
- `pip install pyrgg==1.6`
### Source Code
- Download [Version 1.6](https://github.com/sepandhaghighi/pyrgg/archive/v1.6.zip) or [Latest Source ](https://github.com/sepandhaghighi/pyrgg/archive/dev.zip)
- `pip install .`
### Conda
- Check [Conda Managing Package](https://conda.io)
- `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](https://pyinstaller.org/en/v3.3.1/usage.html#windows))
- Download [Exe-Version 1.6](https://github.com/sepandhaghighi/pyrgg/releases/download/v1.6/PYRGG-1.6.exe)
- 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](http://www.diag.uniroma1.it/challenge9/format.shtml)
* [Sample 1](https://www.dropbox.com/s/i80tnwuuv4iyqet/100.gr.gz?dl=0) (100 Vertices , 3KB)
* [Sample 2](https://www.dropbox.com/s/lqk42pwu7o4xauv/1000.gr.gz?dl=0) (1000 Vertices , 13KB)
* [Sample 3](https://www.dropbox.com/s/93dp8cjs6lnu83u/1000000.gr.gz?dl=0) (1000000 Vertices , 7MB)
* [Sample 4](https://www.dropbox.com/s/rrxdc4wt0ldonfk/5000000.gr.gz?dl=0) (5000000 Vertices , 37MB)
### CSV
```
,,
.
.
.
,,
```
* [Document](https://en.wikipedia.org/wiki/Comma-separated_values)
* [Sample 1](https://www.dropbox.com/s/dmld0eadftnatr5/100.csv?dl=0) (100 Vertices , 3KB)
* [Sample 2](https://www.dropbox.com/s/juxah4nwamzdegr/1000.csv?dl=0) (1000 Vertices , 51KB)
### TSV
```
.
.
.
```
* [Document](https://en.wikipedia.org/wiki/Tab-separated_values)
* [Sample 1](https://www.dropbox.com/s/j3zgs4kx2paxe75/100.tsv?dl=0) (100 Vertices , 29KB)
* [Sample 2](https://www.dropbox.com/s/ykagmjgwlpim6dq/1000.tsv?dl=0) (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](https://en.wikipedia.org/wiki/JSON)
* [Sample 1](https://www.dropbox.com/s/yvevoyb8559nytb/100.json?dl=0) (100 Vertices , 26KB)
* [Sample 2](https://www.dropbox.com/s/f6kljlch7p2rfhy/1000.json?dl=0) (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](https://en.wikipedia.org/wiki/YAML)
* [Sample 1](https://www.dropbox.com/s/9seljohtoqjzjzy/30.yaml?dl=0) (30 Vertices , 6KB)
* [Sample 2](https://www.dropbox.com/s/wtfh38rgmn29npi/100.yaml?dl=0) (100 Vertices , 35KB)
### Weighted Edge List
```
.
.
.
```
* [Document](http://www.cs.cmu.edu/~pbbs/benchmarks/graphIO.html)
* [Sample 1](https://www.dropbox.com/s/moie1xb2wj90y33/100.wel?dl=0) (100 Vertices , 5KB)
* [Sample 2](https://www.dropbox.com/s/h6pohl60okhdnt7/1000.wel?dl=0) (1000 Vertices , 192KB)
### ASP
```
node(1).
.
.
.
node(n).
edge(head_1,tail_1,weight_1).
.
.
.
edge(head_n,tail_n,weight_n).
```
* [Document](https://www.mat.unical.it/aspcomp2013/MaximalClique)
* [Sample 1](https://www.dropbox.com/s/4bufa1m4uamv48z/100.lp?dl=0) (100 Vertices , 7KB)
* [Sample 2](https://www.dropbox.com/s/w79fh1qva64namw/1000.lp?dl=0) (1000 Vertices , 76KB)
### Trivial Graph Format
```
1
.
.
.
n
#
1 2 weight_1
.
.
.
n k weight_n
```
* [Document](https://en.wikipedia.org/wiki/Trivial_Graph_Format)
* [Sample 1](https://www.dropbox.com/s/tehb6f3gz2o5v9c/100.tgf?dl=0) (100 Vertices , 4KB)
* [Sample 2](https://www.dropbox.com/s/9mjeq4w973189cc/1000.tgf?dl=0) (1000 Vertices , 61KB)
### UCINET DL Format
```
dl
format=edgelist1
n=
data:
1 2 weight_1
.
.
.
n k weight_n
```
* [Document](https://sites.google.com/site/ucinetsoftware/home)
* [Sample 1](https://www.dropbox.com/s/82wrl86uowwjud2/100.dl?dl=0) (100 Vertices , 8KB)
* [Sample 2](https://www.dropbox.com/s/kbzbsy47uvfqdsi/1000.dl?dl=0) (1000 Vertices , 729KB)
### Matrix Market
```
%%MatrixMarket matrix coordinate real general
.
.
.
```
* [Document](https://math.nist.gov/MatrixMarket/formats.html)
* [Sample 1](https://www.dropbox.com/s/ztw3vg0roups82q/100.mtx?dl=0) (100 Vertices , 59KB)
* [Sample 2](https://www.dropbox.com/s/skjjvbbzrpvryl4/1000.mtx?dl=0) (1000 Vertices , 1.8MB)
### Graph Line
```
: : ... :
: : ... :
.
.
.
: : ... :
```
* [Sample 1](https://www.dropbox.com/s/obmmb5nw1lca9z3/100.gl?dl=0) (100 Vertices , 17KB)
* [Sample 2](https://www.dropbox.com/s/intufsbudnmfv8m/1000.gl?dl=0) (1000 Vertices , 2.4MB)
### 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](https://www.dropbox.com/s/7dqox0f8e1f859s/100.gdf?dl=0) (100 Vertices , 21KB)
* [Sample 2](https://www.dropbox.com/s/xabjzpp0p5sr4b9/1000.gdf?dl=0) (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](https://en.wikipedia.org/wiki/Graph_Modelling_Language)
* [Sample 1](https://www.dropbox.com/s/g9uvywn1fwt9aq7/100.gml?dl=0) (100 Vertices , 120KB)
* [Sample 2](https://www.dropbox.com/s/5gt5udezy56mlz9/1000.gml?dl=0) (1000 Vertices , 2.4MB)
### GEXF
```
PyRGG
File Name
...
...
```
* [Document](https://github.com/gephi/gexf/wiki/Basic-Concepts#network-topology)
* [Sample 1](https://www.dropbox.com/s/kgx8xl9j0dpk4us/100.gexf?dl=0) (100 Vertices , 63KB)
* [Sample 2](https://www.dropbox.com/s/7a380kf35buvusr/1000.gexf?dl=0) (1000 Vertices , 6.4MB)
### Graphviz
```
graph example
{
node1 -- node2 [weight=W1];
node3 -- node4 [weight=W2];
node1 -- node3 [weight=W3];
.
.
.
}
```
* [Document](https://graphviz.org/doc/info/lang.html)
* [Sample 1](https://www.dropbox.com/s/ukev1hi4kguomri/100.gv?dl=0) (100 Vertices , 11KB)
* [Sample 2](https://www.dropbox.com/s/vpvvliz96mdea1p/1000.gv?dl=0) (1000 Vertices , 106KB)
* [Online Visualization](https://dreampuf.github.io/GraphvizOnline/)
### Pickle
⚠️ Binary format
* [Document](https://docs.python.org/3.10/library/pickle.html)
* [Sample 1](https://www.dropbox.com/s/4s8zt9i13z39gts/100.p?dl=0) (100 Vertices , 12KB)
* [Sample 2](https://www.dropbox.com/s/fzurqu5au0p1b54/1000.p?dl=0) (1000 Vertices , 340KB)
## Issues & Bug Reports
Just fill an issue and describe it. We'll check it ASAP!
or send an email to [[email protected]](mailto:[email protected] "[email protected]").
You can also join our discord server
## Citing
If you use PyRGG in your research, please cite the [JOSS paper](http://joss.theoj.org/papers/da33f691984d9a35f66ff93a391bbc26 "PyRGG 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 ;-) .
