page_title: HITS algorithm
section_title: Algorithm
hierarchical_section_title: HITS algorithm / Algorithm
caption_attribution_description: English: Expanding the root set into a base set. Русский: Расширение корневого набора в базовом.
context_page_description: Hyperlink-Induced Topic Search is a link analysis algorithm that rates Web pages, developed by Jon Kleinberg. The idea behind Hubs and Authorities stemmed from a particular insight into the creation of web pages when the Internet was originally forming; that is, certain web pages, known as hubs, served as large directories that were not actually authoritative in the information that they held, but were used as compilations of a broad catalog of information that led users direct to other authoritative pages. In other words, a good hub represents a page that pointed to many other pages, while a good authority represents a page that is linked by many different hubs.
  The scheme therefore assigns two scores for each page: its authority, which estimates the value of the content of the page, and its hub value, which estimates the value of its links to other pages.
context_section_description: In the HITS algorithm, the first step is to retrieve the most relevant pages to the search query. This set is called the root set and can be obtained by taking the top  pages returned by a text-based search algorithm. A base set is generated by augmenting the root set with all the web pages that are linked from it and some of the pages that link to it. The web pages in the base set and all hyperlinks among those pages form a focused subgraph. The HITS computation is performed only on this focused subgraph. According to Kleinberg the reason for constructing a base set is to ensure that most (or many) of the strongest authorities are included.
  Authority and hub values are defined in terms of one another in a mutual recursion. An authority value is computed as the sum of the scaled hub values that point to that page.  A hub value is the sum of the scaled authority values of the pages it points to.  Some implementations also consider the relevance of the linked pages.
  The algorithm performs a series of iterations, each consisting of two basic steps:
  Authority update: Update each node's authority score to be equal to the sum of the hub scores of each node that points to it.  That is, a node is given a high authority score by being linked from pages that are recognized as Hubs for information.
  Hub update: Update each node's hub score to be equal to the sum of the authority scores of each node that it points to.  That is, a node is given a high hub score by linking to nodes that are considered to be authorities on the subject.
  The Hub score and Authority score for a node is calculated with the following algorithm:
  Start with each node having a hub score and authority score of 1.
  Run the authority update rule
  Run the hub update rule
  Normalize the values by dividing each Hub score by square root of the sum of the squares of all Hub scores, and dividing each Authority score by square root of the sum of the squares of all Authority scores.
  Repeat from the second step as necessary.
  HITS, like Page and Brin's PageRank, is an iterative algorithm based on the linkage of the documents on the web. However it does have some major differences:
  It is query dependent, that is, the (Hubs and Authority) scores resulting from the link analysis are influenced by the search terms;
  As a corollary, it is executed at query time, not at indexing time, with the associated hit on performance that accompanies query-time processing.
  It is not commonly used by search engines. (Though a similar algorithm was said to be used by Teoma, which was acquired by Ask Jeeves/Ask.com.)
  It computes two scores per document, hub and authority, as opposed to a single score;
  It is processed on a small subset of ‘relevant’ documents (a 'focused subgraph' or base set), not all documents as was the case with PageRank.
caption: Expanding the root set into a base set
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