Wiki Web
Exploring Wikipedia's Link Structure
Wiki Web is a fascinating project that combines web scraping, data analysis, and network visualization to explore the interconnected nature of Wikipedia articles. Inspired by the "Getting to Philosophy" phenomenon, this project aims to uncover patterns in how Wikipedia pages link to each other.
Project Overview
The core concept of Wiki Web is simple yet intriguing:
- Start with a random Wikipedia page
- Click the first valid link in the main body of the article (see Getting to Philosophy for what defines a valid link)
- Repeat step 2 until reaching a page that has already been visited
This process creates a chain of linked articles, potentially revealing interesting patterns in Wikipedia's knowledge structure.
Technical Implementation
The project is implemented in Python, leveraging two powerful libraries:
- Beautiful Soup: Used for parsing HTML and extracting relevant links from Wikipedia pages
- NetworkX: Employed to create and analyze the graph structure of linked pages
Challenges and Solutions
One of the most significant challenges in implementing Wiki Web was excluding links within parentheses. This required developing a custom algorithm to match parentheses and determine if a link is enclosed. While the algorithm occasionally fails due to typos in Wikipedia articles (e.g., missing closing parentheses), the error rate is less than 1%, which was deemed acceptable for the current version of the project.
Data Extraction as a Puzzle
As a software engineer, I view data extraction tasks, including web scraping, as puzzles to be solved. Each website presents unique challenges in terms of structure, dynamic content, and anti-scraping measures. Developing Wiki Web has been an excellent opportunity to hone my skills in this area, approaching each obstacle as a new puzzle piece to fit into place.
Future Enhancements
While the README already contains an initial analysis, there's potential for more in-depth exploration:
- Advanced Visualizations: Implementing interactive network graphs using tools like PyVis to better illustrate the connections between articles
- Centrality Analysis: Utilizing NetworkX's centrality algorithms to identify the any other (non-Philosophy) "important" pages in the network
- Topic Clustering: Applying natural language processing techniques to group related articles and visualize topic clusters and see if their centrality differs.