PAGE RANK ALGORITHM This is a Python implementation of the Page Rank algorithm, a widely used algorithm for ranking web pages based on their importance in a network of interconnected pages. The implementation uses NumPy, a powerful numerical computing library in Python, for matrix operations.

FEATURES: Calculates the rank of web pages based on their links and the principle of "voting" by other pages. Handles web pages with no outgoing links or with self-links. Supports customization of the initial rank distribution. Converges to the final rank values based on a specified threshold for convergence. Provides an option to specify the order of pages for ranking.

USAGE: Clone the repository to your local machine. Open the Python file containing the Page Rank algorithm implementation. Modify the pages dictionary to represent the web pages and their links. Each key in the dictionary represents a web page, and the corresponding value is a list of pages that it links to. Optionally, you can modify the pagelist to specify the order in which the pages should be ranked. Run the Python script to execute the Page Rank algorithm. The algorithm will print the rank of each web page in descending order, along with their corresponding page names. Background The Page Rank algorithm was developed by Larry Page and Sergey Brin at Stanford University in 1996 as part of their research on web search engines. It has since become a fundamental algorithm used by many web search engines, including Google, to rank web pages in search results based on their relevance and importance.

REFERENCES: Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab. Python NumPy Library Documentation: https://numpy.org/doc/stable/ Python Language Documentation: https://docs.python.org/3/

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