https://github.com/bashmocha/pagerank-algorithm

PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results
https://github.com/bashmocha/pagerank-algorithm

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PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results

• Host: GitHub
• URL: https://github.com/bashmocha/pagerank-algorithm
• Owner: BashMocha
• Created: 2022-10-10T12:43:12.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-10-10T12:59:44.000Z (over 2 years ago)
• Last Synced: 2024-12-06T09:38:41.248Z (about 2 months ago)
• Language: Python
• Homepage:
• Size: 8.79 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# PageRank Algorithm in Python

## Task:

This algorithhm is one of the projects I've written for [CS50AI](https://cs50.harvard.edu/ai/2020/). You can check my [projects](https://github.com/CheesyFrappe/cs50-ai).


Using a Random Surfer Markov Chain and an Iterative Algorithm, write an AI to rank web pages by importance.

## Background:

Search engines like Google display search results in order of importance using a page-ranking algorithm. The Google PageRank algorithm considers a website as more important (higher page rank), when it is linked to by other imporant websites. Links from less important websites have a lower importance weighting.

## Random Surfer Model:

One way to calculate the PageRanks of a corpus of webpages is to use a random surfer model. This models the behaviour of a web surfer who will start on a random web page and with some probability d (where d is known as the damping factor) randomly click any link on the page, or with with probability 1-d randomly go to any other page in the corpus. This model can be interpreted as a Markov Chain, with each page representing a state, and each page having its own transition model for the probability of moving to any other page next. By sampling a large number states from the Markov Chain, a distribution of the number of visits to each page in the corpus can be obtained, and from it, an estimate of the relative page ranking for each page from 0 to 1 can be calculated.

## Iterative Algorithm:

A page's PageRank can also be calculated using the following recursive mathematical expression:



  



where:

* PR(p) is the PageRank of a given page p

* d is the damping factor

* N is the number of pages in the corpus

* i is each possible page in the corpus

* PR(i) is the PageRank of page i

* NumLinks(i) is the number of links on page i

In order to calculate the PageRank for each page in the corpus, we can start with a basic PageRank for each page of 1 / N, and then iteratively calculate new PageRank values for each page. This continues until PageRank values converge (changes on an iteration are less than a threshold value).

## Usage:

Requires Python(3) to run:

python pagerank.py (corpus_directory)