PageRanking People and Blogs interviews

I recently learned about PageRank, the original Google algorithm for ranking webpages. It works by constructing a Markov chain on links between pages and computing the stationary distribution. The stationary probability of a given page measures the page’s centrality in the web, an index of popularity. I wanted to try implementing the algorithm myself and fiddle with a few Rust libraries, so I wrote a script that runs PageRank on interviews from the People and Blogs series. P&B interviews are a tidy dataset for the algorithm, because Manu always asks interviewees to recommend other blogs, then he uses these recommendations to pick subsequent interviewees. This means the graph is well connected despite its small size. I was going to share the results here, but ranking blogs by popularity seems against the spirit of the indie web ethos, so you’ll have to run the program yourself. (But to make it clear I’m not covering anything up, let me acknowledge that my interview is in a 61-way tie…