Implementing PageRank Algorithm in Python for Web Graph Analysis
Bytes & Brews | Featured scripts have been tested for Python3.4+
In our final publication of 2023 in Bytes & Brews, we will be talking about something most people encounter in their daily lives, but most of us do not know how(or where) the magic happens. This week, we discuss the famous(or now infamous, if you are in SEO), Google PageRank Algorithm.
Introduction
PageRank, an algorithm made famous by Google, measures the importance of web pages in a network by analyzing their link structure. Python, with libraries like NetworkX and BeautifulSoup, provides a powerful environment to delve into such algorithms.
In this guide, we’ll explore how to use Python, NetworkX, and BeautifulSoup to implement PageRank on a collection of interconnected HTML pages. We’ll create a sample network of HTML pages, extract links between them, and then compute PageRank scores to understand the importance of each page within the network.
Step 1: Creating Sample HTML Pages
To begin, let’s create a few sample HTML pages and interlink them to form a small web-like structure. Save these HTML pages in a directory named ‘html_pages’ for demonstration purposes.
HTML Page 1 [index.html]
HTML Page 2 [page1.html]
HTML Page 3 [page2.html]
The HTML pages simulate a simple website with links between the index page and two additional pages (page1.html and page2.html).
Step 2: Setting up the Environment
Before diving into the code, ensure you have the necessary libraries installed..
Step 3: Implementing the PageRank Algorithm
To implement the PageRank algorithm, we will be using NetworkX and BeautifulSoup.
This script consists of two main functions: parse_html() and simple_pagerank(). The parse_html() function reads an HTML file and extracts links using BeautifulSoup. Meanwhile, simple_pagerank() constructs a directed graph representation of the HTML pages and computes their PageRank scores using NetworkX.
Conclusion
This script utilizes NetworkX and BeautifulSoup to analyze a directory of HTML files, constructing a graph representing their link structure and calculating their PageRank scores.
By creating sample HTML pages with different link structures, we’ve visualized how the algorithm assesses the significance of interconnected pages. This understanding can be extended to real-world scenarios, aiding in optimizing website structures and search engine rankings based on PageRank analysis.
Let me know in the comments below once you give it a try on your development environment!
Happy Coding!
