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.

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Installation

PyPI

• Check Python Packaging User Guide
  
• pip install pyrgg==1.8
  

Source Code

• Download Version 1.8 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 1.8
• Run PYRGG-1.8.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 (n)	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 (n)	The total number of vertices in the graph
Probability (p)	The probability for an edge creation between any two vertices
Directed / Undirected	Specifies whether the graph is directed or undirected

Erdős–Rényi

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

Stochastic Block Model

Parameter	Description
Vertices Number (n)	The total number of vertices in the graph
Block Number (k)	The number of blocks (disjoint subsets)
Block Sizes ([|C1|, |C2|, ... |Ck|])	List of block sizes. The should sum up to n (n = |C1|+|C2|+...+|Ck|).
Probability Matrix ([[P11, P12, ..., P1k], ... [Pk1, Pk2, ..., Pkk]])	Edge probabilities for between and within block connections. For undirected graph only the upper triangular indices would be counted.
Directed / Undirected	Specifies whether the graph is directed or undirected
Self Loop / No Self Loop	Specifies whether self-loop is allowed or not

Barabási-Albert

Parameter	Description
Vertices Number (n)	The total number of vertices in the graph
Attaching Edge Number (k)	The number of edges to attach to a new node

Supported Formats

DIMACS

``` p sp a

.
.
.

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)

CSV

``` ,,

.
.
.

<head_n>,<tail_n>,<weight_n>

```

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

TSV

```

.
.
.

<head_n>    <tail_n>    <weight_n>

```

• 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: head1 target: tail1 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

```

.
.
.

<head_n> <tail_n> <weight_n>    

```

• 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=<number of vertices> 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 <number of vertices> <number of vertices> <number of edges> <head_1> <tail_1> <weight_1> . . . <head_n> <tail_n> <weight_n> * Document * Sample 1 (100 Vertices , 59KB) * Sample 2 (1000 Vertices , 1.8MB)

Graph Line

<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>

• Sample 1 (100 Vertices , 17KB)
• Sample 2 (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 (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

<?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>

• 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

Cite

If you use PyRGG in your research, we would appreciate citations to the following paper:

Haghighi, S., 2017. Pyrgg: Python Random Graph Generator. Journal of Open Source Software, 2(17), p.331.

bibtex @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.

16- Barabási, Albert-László, and Réka Albert. "Emergence of scaling in random networks." science 286.5439 (1999): 509-512.

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