This project implements two popular web page ranking algorithms: HITS (Hypertext Induced Topic Search) and PageRank. These algorithms analyze the link structure of a web graph to determine the importance or relevance of individual web pages.
- HITS Algorithm: Calculates authority and hub scores for each node in the web graph.
- PageRank Algorithm: Determines the importance of web pages based on their inbound links and the importance of the linking pages.
- Graph Class: Manages the nodes and edges of the web graph.
- Node Class: Represents a node in the web graph and stores relevant information such as authority, hub, and PageRank scores.
- Utility Functions: Provides functions to initialize the graph from a file, perform iterations of HITS and PageRank algorithms, and retrieve ranking lists.
- Clone the repository:
git clone https://github.com/kajallochab/Link-Relevance-Ranking
cd Link-Relevance-Ranking- Install dependencies:
# If using pip
pip install numpy
# If using conda
conda install numpy- Run the algorithms:
# Run HITS algorithm
python hits.py
# Run PageRank algorithm
python pg_rank.py