https://github.com/rasmussvala/tna010-page-ranking
Course project for TNA010 Matrix Methods for AI at Linköping University about the PageRank algorithm.
https://github.com/rasmussvala/tna010-page-ranking
algorithms linkoping-university liu matlab page-rank-algorithm page-ranking
Last synced: about 2 months ago
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Course project for TNA010 Matrix Methods for AI at Linköping University about the PageRank algorithm.
- Host: GitHub
- URL: https://github.com/rasmussvala/tna010-page-ranking
- Owner: rasmussvala
- Created: 2023-11-23T09:04:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-05T16:35:09.000Z (8 months ago)
- Last Synced: 2025-02-02T22:12:06.663Z (4 months ago)
- Topics: algorithms, linkoping-university, liu, matlab, page-rank-algorithm, page-ranking
- Language: Python
- Homepage:
- Size: 9.47 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# TNA010 - PageRank Algorithm
## Overview
This project implements the PageRank algorithm, originally developed by Google, to rank web pages based on their importance in a network. The implementation is based on the methods described in _Matrix Methods in Data Mining and Pattern Recognition (2nd Edition)_ by Lars Eldén.
## Algorithm Description
PageRank works by modeling web surfing behavior as a probabilistic process. It assigns a numerical weight (rank) to each webpage based on:
1. The number and quality of links pointing to the page
2. The rank of the pages that link to it
The algorithm uses the following key concepts:
- **Sparse Adjacency Matrix**: Represents the web as a directed graph where each element represents the probability of moving from one page to another
- **Power Method**: An iterative approach to compute the final rank values
## Features
- Reads network data from a text file
- Constructs a sparse matrix representation of the web graph
- Implements multiple solving methods:
- Eigenvalue decomposition (for small matrices)
- Standard Power Method
- Optimized Power Method
- Visualizes results with a bar plot
- Identifies and displays top-ranked pages
## Prerequisites
- MATLAB R2019b or later
## Installation
1. Clone this repository:
```
git clone https://github.com/rasmussvala/TNA010-Page-Ranking.git
```
2. Navigate to the project directory in MATLAB
## Usage
- Run the algorithm by running: PageRank.mlx
- Change data at the top of PageRank.mlx `fileID = fopen('Data/data-course-book-1.txt', 'r');`