GitHub - Amayes985-stack/Stanford-Algorithm-Project: C++ toolkit for analyzing and visualizing large-scale graphs sourced from the Stanford Large Network Dataset Collection.

Introduction

The goal of this project is to study various parameters of large-scale graphs sourced from the web. Typically, these graphs are characterized by a high number of edges and vertices, making their analysis complex and computationally demanding. The focus will be on exploring graphs from the Stanford Large Network Dataset Collection, which models diverse social networks. These graphs are typically represented in text files containing information about their edges and vertices.

Overview

This GitHub repository contains a C++ toolkit for analyzing and visualizing large-scale graphs sourced from the Stanford Large Network Dataset Collection.

Key Features

Graph Parameter Calculation: Analyze various parameters such as vertices, edges, max degree, connected components, and cycles with 3 vertices.
Degree Distribution Visualization: Create graphical representations of the degree distribution for each graph.
Degeneracy Computation: Calculate the degeneracy for each graph using an iterative removal approach.

Usage

Clone the Repository:

git clone https://github.com/Amayes985-stack/Stanford-Algorithm-Project.git

Compile and Run: Follow the instructions in the README file for compilation and execution.

Graph Format

Make sure to review and modify the format of graphs from the Stanford database as needed.

Contribution Guidelines

Fork the repository and create a new branch for your contributions.
Make changes and submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE.md file for details.

Acknowledgments

We appreciate your contributions to advancing graph analysis. Thank you to all contributors!

=======================================================================
Graph Analysis with SNAP - README
=======================================================================

This directory contains a graph analysis program using the SNAP (Stanford Network Analysis Platform) library. The code is written in C++, allowing extraction of various structural and topological properties from directed or undirected graphs.

## Directory Contents

- **graph_analysis_PNG.cpp**: Main program for analyzing directed graphs.
- **graph_analysis_PUNG.cpp**: Main program for analyzing undirected graphs.
- **Makefile**: File for compilation using non-Microsoft compilers.
- **csv_to_txt_twitch.py**: Python script to convert Twitch CSV file to Small World format.
- **csv_to_txt_github.py**, **csv_to_txt_wikipedia.py**, **csv_to_txt_facebook.py**: Similar Python scripts for other datasets.
- **README.txt**: This file.
- **result_pung.txt**: Execution result of the ./graph_parameters_PUNG displayed in an array 
- **result_png.txt**: Execution result of the ./graph_parameters_PNG displayed in an array
- **graph_name_degree_distribution.png** : Graph Display -> Degree and Frequency Distribution 
- **graphe_name_degree_distribution.dat** : Degree and Frequency Distribution
- **participants.txt** : Participants who contributed to this project 


## Graph Data Formats

The xlsx files for Facebook, Twitch, Wikipedia, and GitHub represent graphs in Excel format. Ensure these files are converted to an appropriate edge-list format (e.g., CSV) before using the analysis programs.

## Compilation and Execution

Ensure that you have the SNAP library installed before compiling the programs. You can use the provided Makefile for compilation with non-Microsoft compilers. The generated executables are `graph_parameters_PNG` and `graph_parameters_PUNG`.

To generate graphs from CSV files, run the Python scripts `csv_to_txt_*.py` before executing the analysis programs.

```bash
make generate_graphs
make execute
make display_graph

Usage

The analysis programs take edge-list graph files as input and display various graph properties. For example, to analyze the directed graphs use the command:

g++ -o graph_parameters_PNG graph_analysis_PNG.cpp -I/workspaces/Stanford-Algorithm-Project/snap-core -I/workspaces/Stanford-Algorithm-Project/glib-core -lsnap -lrt -fopenmp
./graph_parameters_PNG

To analyze the undirected graphs, use the command:

g++ -o graph_parameters_PUNG graph_analysis_PUNG.cpp -I/workspaces/Stanford-Algorithm-Project/snap-core -I/workspaces/Stanford-Algorithm-Project/glib-core -lsnap -lrt -fopenmp
./graph_parameters_PUNG

To display the distribution of directed graphs, use the command :

g++ -o degree_distribution_PNG degree_distribution_PNG.cpp -I/workspaces/Stanford-Algorithm-Project/snap-core -I/workspaces/Stanford-Algorithm-Project/glib-core -lsnap -lrt -fopenmp
./degree_distribution_PNG

To display the distribution of undirected graphs, use the command :

g++ -o degree_distribution_PUNG degree_distribution_PUNG.cpp -I/workspaces/Stanford-Algorithm-Project/snap-core -I/workspaces/Stanford-Algorithm-Project/glib-core -lsnap -lrt -fopenmp
./degree_distribution_PUNG

Make sure to specify paths to snap-core and glib-core libraries at runtime and that -lsnap, -lrt and -fopenmp libraries are installed

Algorithmic Complexity

The program utilizes various features of the SNAP library to compute properties such as the number of nodes, number of edges, maximum degree, strongly connected components, cycles, and degeneracy. The complexity depends on specific operations and the structure of the graph.

Notes

For optimal performance, the SNAP library uses efficient data structures to represent graphs.
Specific code sections include graph type detection, maximum degree calculation, connected components calculation, cycle calculation, and degeneracy calculation.

Cleaning Up

To clean generated files, use the command:

make clean

Happy graph exploration!


Make sure to adjust details based on your project, such as file names, compilation commands, etc.
=======================================================================
Analyse de Graphe avec SNAP - README
=======================================================================

Ce répertoire contient un programme d'analyse de graphe utilisant la bibliothèque SNAP (Stanford Network Analysis Platform). Le code est écrit en C++, permettant l'extraction de diverses propriétés structurelles et topologiques de graphes orientés ou non orientés.

## Contenu du Répertoire

- **graph_analysis_PNG.cpp**: Programme principal pour l'analyse de graphes orientés.
- **graph_analysis_PUNG.cpp**: Programme principal pour l'analyse de graphes non orientés.
- **Makefile**: Fichier pour la compilation avec des compilateurs non-Microsoft.
- **csv_to_txt_twitch.py**: Script Python pour convertir le fichier CSV Twitch en format Small World.
- **csv_to_txt_github.py**, **csv_to_txt_wikipedia.py**, **csv_to_txt_facebook.py**: Scripts Python similaires pour d'autres ensembles de données.
- **README.txt**: Ce fichier.
- **result_pung.txt** : Résultat d’exécution du . /graph_parameters_PUNG affiché dans un tableau 
- **result_png.txt** : Résultat d’exécution du . /graph_parameters_PNG affiché dans un tableau
- **graph_name_degree_distribution.png** : Affichage graphique -> Distribution de degrés et de fréquences 
- **graphe_name_degree_distribution.dat** : Distribution des degrés et des fréquences
- **participants.txt** : Participants ayant contribué à ce projet


## Formats de Données de Graphe

Les fichiers xlsx de Facebook, Twitch, Wikipedia et GitHub représentent des graphes au format Excel. Assurez-vous de convertir ces fichiers dans un format approprié de liste d'arêtes (par exemple, CSV) avant d'utiliser les programmes d'analyse.

## Compilation et Exécution

Assurez-vous d'avoir la bibliothèque SNAP installée avant de compiler les programmes. Vous pouvez utiliser le Makefile fourni pour la compilation avec des compilateurs non-Microsoft. Les exécutables générés sont `graph_parameters_PNG` et `graph_parameters_PUNG`.

Pour générer des graphes à partir de fichiers CSV, exécutez les scripts Python `csv_to_txt_*.py` avant de lancer les programmes d'analyse.

```bash
make generate_graphs
make execute

Utilisation

Les programmes d'analyse prennent en entrée des fichiers de graphe au format liste d'arêtes et affichent diverses propriétés du graphe. Par exemple, pour analyser les graphes orientés, utilisez la commande :

g++ -o graph_parameters_PNG graph_analysis_PNG.cpp -I/workspaces/Stanford-Algorithm-Project/snap-core -I/workspaces/Stanford-Algorithm-Project/glib-core -lsnap -lrt -fopenmp
./graph_parameters_PNG

Pour analyser les graphes non orientés, utiliser la commande :

g++ -o graph_parameters_PUNG graph_analysis_PUNG.cpp -I/workspaces/Stanford-Algorithm-Project/snap-core -I/workspaces/Stanford-Algorithm-Project/glib-core -lsnap -lrt -fopenmp
./graph_parameters_PUNG

Pour afficher la distribution de degrés et de fréquence des graphes orientés, utiliser la commande :

g++ -o degree_distribution_PNG degree_distribution_PNG.cpp -I/workspaces/Stanford-Algorithm-Project/snap-core -I/workspaces/Stanford-Algorithm-Project/glib-core -lsnap -lrt -fopenmp
./degree_distribution_PNG

Pour afficher la distribution de degrés et de fréquence des graphes non orientés, utiliser la commande :

g++ -o degree_distribution_PNG degree_distribution_PNG.cpp -I/workspaces/Stanford-Algorithm-Project/snap-core -I/workspaces/Stanford-Algorithm-Project/glib-core -lsnap -lrt -fopenmp
./degree_distribution_PNG

Assurez-vous de spécifier les chemins vers les bibliothèques snap-core et glib-core lors de l'exécution et que les libraires -lsnap, -lrt et -fopenmp sont bien installés

Complexité Algorithmique

Le programme utilise diverses fonctionnalités de la bibliothèque SNAP pour calculer des propriétés telles que le nombre de nœuds, le nombre d'arêtes, le degré maximal, les composantes fortement connexes, les cycles et la dégénérescence. La complexité dépend des opérations spécifiques et de la structure du graphe.

Notes

Pour des performances optimales, la bibliothèque SNAP utilise des structures de données efficaces pour représenter les graphes.
Les sections spécifiques du code incluent la détection du type de graphe, le calcul du degré maximal, le calcul des composantes connexes, le calcul des cycles et le calcul de la dégénérescence.

Nettoyage

Pour supprimer les fichiers générés, utilisez la commande :

make clean

Bonne exploration de graphes !


Assurez-vous d'ajuster les détails en fonction de votre projet, tels que les noms de fichiers, les commandes de compilation, etc.


Bonne exploration de graphes !

Assurez-vous d'ajuster les détails en fonction de votre projet, tels que les noms de fichiers, les commandes de compilation, etc.

Name		Name	Last commit message	Last commit date
Latest commit History 33 Commits
glib-core		glib-core
snap-core		snap-core
Amayes-Djermoune-Rapport.pdf		Amayes-Djermoune-Rapport.pdf
CA-GrQc.txt		CA-GrQc.txt
CA-GrQc_degree_distribution.dat		CA-GrQc_degree_distribution.dat
CA-GrQc_degree_distribution.png		CA-GrQc_degree_distribution.png
FacebookSites.csv		FacebookSites.csv
FacebookSites.txt		FacebookSites.txt
FacebookSites.xlsx		FacebookSites.xlsx
GitHub.csv		GitHub.csv
GitHub.txt		GitHub.txt
GitHub.xlsx		GitHub.xlsx
GitHub_degree_distribution.dat		GitHub_degree_distribution.dat
GitHub_degree_distribution.png		GitHub_degree_distribution.png
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
Wikipedia1.csv		Wikipedia1.csv
Wikipedia1.txt		Wikipedia1.txt
Wikipedia1.xlsx		Wikipedia1.xlsx
Wikipedia1_degree_distribution.dat		Wikipedia1_degree_distribution.dat
Wikipedia1_degree_distribution.png		Wikipedia1_degree_distribution.png
csv_to_txt_facebook.py		csv_to_txt_facebook.py
csv_to_txt_github.py		csv_to_txt_github.py
csv_to_txt_twitch.py		csv_to_txt_twitch.py
csv_to_txt_wikipedia.py		csv_to_txt_wikipedia.py
degree_distribution_PNG.cpp		degree_distribution_PNG.cpp
degree_distribution_PUNG.cpp		degree_distribution_PUNG.cpp
graph_analysis_PNG.cpp		graph_analysis_PNG.cpp
graph_analysis_PUNG.cpp		graph_analysis_PUNG.cpp
participants.txt		participants.txt
result_CA.txt		result_CA.txt
result_Facebook.txt		result_Facebook.txt
result_GitHub.txt		result_GitHub.txt
result_Twitch.txt		result_Twitch.txt
result_Wikipedia.txt		result_Wikipedia.txt
result_png.txt		result_png.txt
result_pung.txt		result_pung.txt
twitchDE.csv		twitchDE.csv
twitchDE.txt		twitchDE.txt
twitchDE.xlsx		twitchDE.xlsx
twitchDE_degree_distribution.dat		twitchDE_degree_distribution.dat
twitchDE_degree_distribution.png		twitchDE_degree_distribution.png

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Amayes985-stack/Stanford-Algorithm-Project

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Introduction

Overview

Key Features

Usage

Graph Format

Contribution Guidelines

License

Acknowledgments

Usage

Algorithmic Complexity

Notes

Cleaning Up

Utilisation

Complexité Algorithmique

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