Topic
5 videos.
The idea behind link analysis is simple. A page that many other pages link to may be worth reading. Link analysis turns that hunch into maths, treating the web as a network of pages joined by links and scoring each page by its place in it. PageRank is the best-known example, and HITS is another.
The videos here come from university courses, so they teach the method itself and say little about running a website. How Google's PageRank Algorithm Works is a short introduction that goes easy on the maths.
IR Course Lecture 16 sets PageRank beside HITS. The remaining lectures use linear algebra and graph theory, and suit readers happy with matrices. They describe the classic algorithm, not how Google ranks pages today.
Teaching or conference talk
PageRank, random walks and matrix factorisation are worked through in a Stanford lecture from Jure Leskovec, which treats graphs as matrices and builds towards node embeddings. Lecture 4.1 of the CS224W course.
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PageRank and HITS are two classic ways to score how important a web page is, and this lecture works through both, including the random surfer idea and Markov chains.
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What makes a web page important to PageRank? This short video explains the random surfer idea and what the damping factor is for.
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A Georgia Tech lecture works through PageRank with a small numbers example, first without teleportation and then with it, before moving to huge matrices and the personalised version.
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An Imperial College London lecturer introduces PageRank through linear algebra, in week 5 of a Mathematics for Machine Learning course on Coursera.
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