This repository contains the implementation of a network analysis project using graph theory concepts and algorithms. The project is part of the Data Structures course at National University of Computer and Emerging Sciences, FAST School of Computing, Spring 2021, Islamabad Campus.

The objective of this project is to analyze different datasets using graph theory techniques and algorithms. The project focuses on understanding and investigating hidden patterns within the data through the representation and analysis of graphs. The datasets provided for the project involve collaboration networks in the fields of General Relativity and Quantum Cosmology, Astro Physics, and Amazon product co-purchasing. The tasks involve computing various graph statistics, degree distributions, and identifying connected components.

The project provides three datasets for analysis:

General Relativity and Quantum Cosmology collaboration network dataset from the arXiv. Astro Physics collaboration network dataset from the arXiv. Amazon product co-purchasing network and ground-truth communities dataset. Each dataset contains information about the collaborations between authors, papers submitted, and product co-purchasing relationships.

To run the project, you need:

Visual Studio 2019 or later. C++ programming language. Knowledge of graph theory and algorithms. Understanding of data structures and algorithms covered in the course.

Clone the repository:

Open the project in Visual Studio 2019.

Build the project and ensure there are no errors.

Run the program, and a cool menu will be displayed to perform the tasks individually or all at once.

Follow the instructions on the menu to select a dataset, choose a task, and view the results.

The program will display various statistics, distributions, and information about the graph structure and components based on the selected task.

You can modify the code and algorithms to enhance the project's functionality or explore additional analysis techniques.