HITS algorithm
E336027
The HITS algorithm is a link analysis method that ranks web pages by separately evaluating their authority and hub scores based on the structure of hyperlinks.
All labels observed (2)
| Label | Occurrences |
|---|---|
| HITS algorithm canonical | 2 |
| Hyperlink-Induced Topic Search | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T3196727 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HITS algorithm Context triple: [PageRank, relatedConcept, HITS algorithm]
-
A.
PageRank algorithm
The PageRank algorithm is a link analysis method used by search engines, notably Google, to rank web pages in search results based on their importance within the web’s link structure.
-
B.
The Anatomy of a Large-Scale Hypertextual Web Search Engine
"The Anatomy of a Large-Scale Hypertextual Web Search Engine" is a seminal research paper by Sergey Brin and Larry Page that introduced the design and PageRank algorithm behind the early Google search engine.
-
C.
Tarjan's strongly connected components algorithm
Tarjan's strongly connected components algorithm is a classic linear-time graph algorithm that efficiently identifies all strongly connected components in a directed graph using depth-first search and low-link values.
-
D.
Eigenfactor Score
Eigenfactor Score is a journal influence metric that estimates the importance of scholarly journals by considering the origin and frequency of citations in a network-based model.
-
E.
Resnik
Resnik is a surname most notably associated with Judith Resnik, the American astronaut who died in the Space Shuttle Challenger disaster.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: HITS algorithm Target entity description: The HITS algorithm is a link analysis method that ranks web pages by separately evaluating their authority and hub scores based on the structure of hyperlinks.
-
A.
PageRank algorithm
The PageRank algorithm is a link analysis method used by search engines, notably Google, to rank web pages in search results based on their importance within the web’s link structure.
-
B.
The Anatomy of a Large-Scale Hypertextual Web Search Engine
"The Anatomy of a Large-Scale Hypertextual Web Search Engine" is a seminal research paper by Sergey Brin and Larry Page that introduced the design and PageRank algorithm behind the early Google search engine.
-
C.
Tarjan's strongly connected components algorithm
Tarjan's strongly connected components algorithm is a classic linear-time graph algorithm that efficiently identifies all strongly connected components in a directed graph using depth-first search and low-link values.
-
D.
Eigenfactor Score
Eigenfactor Score is a journal influence metric that estimates the importance of scholarly journals by considering the origin and frequency of citations in a network-based model.
-
E.
Resnik
Resnik is a surname most notably associated with Judith Resnik, the American astronaut who died in the Space Shuttle Challenger disaster.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
graph algorithm
ⓘ
link analysis algorithm ⓘ web search algorithm ⓘ |
| alsoKnownAs |
Hubs and Authorities algorithm
ⓘ
HITS algorithm ⓘ
surface form:
Hyperlink-Induced Topic Search
|
| application |
analyzing social networks
ⓘ
community detection in graphs ⓘ identifying authoritative sources in citation networks ⓘ ranking web pages for a specific topic ⓘ |
| assumes | links represent endorsements or recommendations ⓘ |
| basedOn | hyperlink structure ⓘ |
| citationTitle | Authoritative sources in a hyperlinked environment ⓘ |
| computes |
eigenvectors of A-A-transpose
ⓘ
eigenvectors of A-transpose-A ⓘ |
| convergesTo |
principal eigenvector of A-A-transpose for hub scores
ⓘ
principal eigenvector of A-transpose-A for authority scores ⓘ |
| coreIdea |
good authorities are pointed to by good hubs
ⓘ
good hubs point to good authorities ⓘ |
| creator | Jon Kleinberg ⓘ |
| differentFrom |
PageRank algorithm
ⓘ
surface form:
PageRank
|
| distinctionFromPageRank |
HITS computes separate hub and authority scores
ⓘ
HITS is typically query-dependent ⓘ PageRank is typically query-independent ⓘ |
| field |
information retrieval
ⓘ
network analysis ⓘ web mining ⓘ |
| influenced | subsequent link analysis methods ⓘ |
| input |
adjacency matrix of hyperlinks
ⓘ
directed graph of web pages ⓘ |
| limitation |
topic drift
ⓘ
vulnerability to link spam ⓘ |
| mathematicalFoundation |
linear algebra
ⓘ
spectral graph theory ⓘ |
| output |
authority scores for nodes
ⓘ
hub scores for nodes ⓘ |
| publicationYear | 1999 ⓘ |
| publishedIn | Journal of the ACM ⓘ |
| relatedTo |
PageRank algorithm
ⓘ
surface form:
PageRank
|
| requires | connected subgraph around query results ⓘ |
| step |
construct root set from search results
ⓘ
expand to base set by adding in-linking and out-linking pages ⓘ iteratively update hub and authority scores until convergence ⓘ |
| updateRule |
authority score is proportional to sum of hub scores of linking pages
ⓘ
hub score is proportional to sum of authority scores of linked pages ⓘ |
| usedIn | early web search engines research ⓘ |
| usesConcept |
authority score
ⓘ
hub score ⓘ |
| usesMethod |
iterative refinement
ⓘ
power iteration ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: HITS algorithm Description of subject: The HITS algorithm is a link analysis method that ranks web pages by separately evaluating their authority and hub scores based on the structure of hyperlinks.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
PageRank
this entity surface form:
Hyperlink-Induced Topic Search