Google Pagerank Algoritham

• Tech Stack: Linear Algebra

PageRank Algorithm: Exploring Web Page Ranking

Google PageRank is a link analysis algorithm developed by Larry Page and Sergey Brin, the founders of Google, that revolutionized web search and web indexing and became a cornerstone of the company's success. PageRank's objective is to quantify the relative authority and relevance of web pages so that search engines can rank and retrieve search results in a way that meets users' information demands. PageRank utilizes a sophisticated mathematical framework that uses concepts from linear algebra, particularly eigenvalues and eigenvectors, that calculate the importance scores of web pages.

Project Overview

In this project, we delve into the mathematics and algorithms that underlie PageRank, exploring how principles from graph theory and linear algebra are used to rate the significance of web sites.

Key Components

• PageRank Algorithm: Understand the fundamentals of the PageRank algorithm and its importance in web search.
• Mathematical Framework: Explore the mathematical concepts used in PageRank, including eigenvalues and eigenvectors.
• Graph Theory: Learn how graph theory concepts are applied to model the web graph and calculate page importance.
• Implementation: Explore implementation strategies and algorithms for calculating PageRank scores.
• Applications: Discuss real-world applications and implications of PageRank in web search and information retrieval.