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.
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- Check Python Packaging User Guide
pip install pyrgg==1.6
- Download Version 1.6 or Latest Source
pip install .
- Check Conda Managing Package
conda install -c sepandhaghighi pyrgg
.dlls(for more information visit here)
- Download Exe-Version 1.6
- Run
PYRGG-1.6.exe
PyRGG will likely run on a modern dual core PC. Typical configuration is:
- Dual Core CPU (2.0 Ghz+)
- 4GB of RAM
- Open
CMD(Windows) orTerminal(Linux) - Run
pyrggorpython -m pyrgg(or runPYRGG.exe) - Enter data
| 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 |
| 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 |
| 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 |
p sp <number of vertices> <number of edges>
a <head_1> <tail_1> <weight_1>
.
.
.
a <head_n> <tail_n> <weight_n>
- Document
- Sample 1 (100 Vertices , 3KB)
- Sample 2 (1000 Vertices , 13KB)
- Sample 3 (1000000 Vertices , 7MB)
- Sample 4 (5000000 Vertices , 37MB)
<head_1>,<tail_1>,<weight_1>
.
.
.
<head_n>,<tail_n>,<weight_n>
<head_1> <tail_1> <weight_1>
.
.
.
<head_n> <tail_n> <weight_n>
{
"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
}
]
}
}
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
<head_1> <tail_1> <weight_1>
.
.
.
<head_n> <tail_n> <weight_n>
node(1).
.
.
.
node(n).
edge(head_1,tail_1,weight_1).
.
.
.
edge(head_n,tail_n,weight_n).
1
.
.
.
n
#
1 2 weight_1
.
.
.
n k weight_n
dl
format=edgelist1
n=<number of vertices>
data:
1 2 weight_1
.
.
.
n k weight_n
%%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>
<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>
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
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
]
]
<?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>
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
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
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 |  |
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.
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