PageRank From Scratch

This repository contains a notebook-based implementation of PageRank from scratch, along with datasets and report material.

Repository Contents

• pagerank.ipynb: main PageRank implementation notebook.
• hollins.dat: input graph or link structure data.
• hollins_pagerank.csv: generated ranking output.
• Homeworks.pdf, googleFinalVersionFixed.pdf: supporting assignment or report documents.

Topics Covered

• PageRank
• graph ranking
• iterative linear algebra methods
• ranking from raw graph data

Getting Started

1. Clone the repository.
2. Open pagerank.ipynb in Jupyter.
3. Run the notebook to reproduce the ranking calculations.
4. Use the PDF files for theory, assignment context, or reporting.

Notes

This repository is focused on educational implementation of the PageRank algorithm rather than on a production search system.