Google Page Ranking Algorithm

A Python implementation of the PageRank algorithm to evaluate the relative importance of pages in a corpus of HTML files.

Overview

• Parse a directory of HTML pages to build a link graph (corpus).
• Compute PageRank using:
  • Sampling (random surfer simulation).
  • Iterative update until convergence.
• Handle dangling pages (pages without outgoing links).
• Output normalized PageRank scores for each page.
• Configurable damping factor and sample size.

Usage

Clone the repository and run the script with a directory containing HTML files.

Windows (PowerShell / CMD)

py pagerank.py corpus

macOS / Linux (Terminal)

python3 pagerank.py corpus

Example Session

$ python3 pagerank.py corpus0
PageRank Results from Sampling (n = 10000)
  1.html: 0.2200
  2.html: 0.3900
  3.html: 0.3900
PageRank Results from Iteration
  1.html: 0.2187
  2.html: 0.3906
  3.html: 0.3906

Notes

• Default damping factor: 0.85
• Default sample size: 10,000
• Pure Python implementation; no external dependencies required