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HITS algorithm

Hyperlink-Induced Topic Search (HITS; also known as hubs and authorities) 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…

History & Art

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Research this topic

Explore the main themes, entities and connections around HITS algorithm. Start with the topic map, then use the sections below for research and deeper semantic analysis.

Explore this topic

Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.

History

Impact factorScienceNatureInternetYahoo!
8 related topics

Algorithm

Search algorithmMutual recursionSquare rootPageBrin
10 related topics

In detail

Normalize
1 related topics

Pseudocode

Pseudocode
1 related topics

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

History

Algorithm

In detail

Pseudocode

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

HITS algorithm

Nodes29
Edges28
Triples13
Avg. degree1.93
Density0.068966
Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

HITS algorithm

Top relations

related to Steps · 10
HITS algorithm → According, An, Authority, HITS, In, Kleinberg, Some, The, The HITS, This

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

hub authority pages page algorithm web update sum score scores values also search two hubs rule link set node hits

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Scienceinstance ofJournals0.80text
Nature are filled with numerous citationsinstance ofJournals0.80text
making these magazines have very high impact factorsinstance ofJournals0.80text
HITS algorithmrelated to StepsIn0.60section
HITS algorithmrelated to StepsHITS0.60section
HITS algorithmrelated to StepsThis0.60section
HITS algorithmrelated to StepsThe0.60section
HITS algorithmrelated to StepsThe HITS0.60section
HITS algorithmrelated to StepsAccording0.60section
HITS algorithmrelated to StepsKleinberg0.60section
HITS algorithmrelated to StepsAuthority0.60section
HITS algorithmrelated to StepsAn0.60section

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

    Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

    Min side: 3
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