SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two Markov chains, with pages as either hubs or authorities. It computes hub and authority scores to find the principal community of related pages. PageRank also models the web as a graph, and defines a page's rank based on the ranks of pages that link to it, including a random surfer model to avoid rank sinks. Both leverage random walks to determine important pages based on the web's link structure.

1. Motivation When searching for information on the WWW, user perform a query to a search engine. The engine return, as the query’s result, a list of Web sites which usually is a huge set. So the ranking of these web sites is very important. Because much information is contained in the link-structure of the WWW, information such as which pages are linked to others can be used to augment search algorithms.

4. SALSA----Idea SALSA is based upon the theory of Markov chains, and relies on the stochastic properties of random walks performed on our collection of sites. The input to our scheme consists of a collection of sites C which is built around a topic t . Intuition suggests that authoritative sites on topic t should be visible from many sites in the subgraph induced by C . Thus, a random walk on this subgraph will visit t -authorities with high probability.

5. SALSA----Idea Combine the theory of random walks with the notion of the two distinct types of Web sites, hubs and authorities, and actually analyze two different Markov chains: A chain of hubs and a chain of authorities. Analyzing both chains allows our approach to give each Web site two distinct scores, a hub score and an authority score.

7. SALSA the principal community of authorities(hubs) found by the SALSA will be composed of the sites whose entries in the principal eigenvector of A ( H ) are the highest.

8. SALSA----Conclusion SALSA is a new stochastic approach for link structure analysis, which examines random walks on graphs derived from the link structure. The principal community of authorities(hubs) corresponds to the sites that are most frequently visited by the random walk defined by the authority(hub) Markov chain.

9. The PageRank Citation Ranking: Bringing Order to the Web Larry Page etc. Stanford University

10. PageRank----Idea Every page has some number of forward links(outedges) and backlinks(inedges)

13. PageRank----Definition u: a web page F u : set of pages u points to B u : set of pages that point to u N u =|F u |: the number of links from u c: a factor used for normalization The equation is recursive, but it may be computed by starting with any set of ranks and iterating the computation until it converges.

14. PageRank----definition A problem with above definition: rank sink If two web pages point to each other but to no other page, during the iteration, this loop will accumulate rank but never distribute any rank.

15. PageRank----definition Definition modified: E(u) is some vector over the web pages(for example uniform, favorite page etc.) that corresponds to a source of rank. E(u) is a user designed parameter.