The document discusses various link analysis techniques for web search, including: 1) HITS identifies hubs and authorities through an iterative algorithm that computes mutually reinforcing scores; authorities are pages pointed to by many hubs and hubs point to many authorities. 2) PageRank models a random surfer on the web and ranks pages by the probability that the surfer would be at that page; it was key to Google's early success over other search engines. 3) Personalized PageRank can bias results based on a user's homepage and related pages.

1. Web Search Advances & Link Analysis

19. Illustrated Update Rules 2 3 a 4 = h 1 + h 2 + h 3 1 5 7 6 4 4 h 4 = a 5 + a 6 + a 7

31. Sample Stable Fixpoint 0.4 0.4 0.2 0.2 0.2 0.2 0.4

35. PageRank Algorithm Let S be the total set of pages. Let  p  S: E ( p ) =  /| S| (for some 0<  <1 , e.g. 0.15 ) Initialize  p  S: R ( p ) = 1/| S| Until ranks do not change (much) ( convergence ) For each p  S: For each p  S: R ( p ) = cR ´ ( p ) ( normalize )