This project implements Google's PageRank algorithm, a cornerstone of web search, which measures the importance of pages by analyzing their hyperlink structure. It offers two distinct methods: random sampling and iterative computation for estimating PageRank scores. By leveraging both methods, this project provides a comprehensive solution for analyzing and ranking web pages within a given corpus by their probability of being visited.
You'll need to have Python installed. You can download it from the official Python website.
- Clone the repository:
git clone https://github.com/ColinDao/cite.git- Navigate to the project directory:
cd citeTo analyze a corpus' pages, run the following command:
python pagerank.py [corpus0]Adjust the sampling size and damping factor to tinker with how many random iterations you'd like to execute and the likelihood of choosing a link on that page.
Random Sampling: The random sampling method begins by selecting a page randomly from the corpus. Utilizing a transition model and Markov Chain,
the current page's state transitions to a random link within that page. The visits per page are recorded and accumulate throughout the total sample iterations,
providing a model of the probability distribution as if a random internet surfer were navigating the web.
Iterative Sampling: In the iterative sampling approach, PageRank values are computed based on previous iterations.
Initially, each page is assigned an equal probability score—that is choosing one randomly. Then, using a recursive mathematical expression derived from the PageRank algorithm,
the probability score for each page is updated based on the PageRank values of its parent pages. This process continues iteratively until the
PageRank values deviate by less than 0.1%. By refining the PageRank values with each iteration, the iterative sampling method provides an accurate
estimation of the importance of each page within the corpus.
Language: Python
Libraries: OS, Random, RE, Sys
This project was completed as a part of CS50's Introduction to Artificial Intelligence with Python. Go check them out!
MIT License
Copyright (c) 2024 Colin Dao
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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