Jon Kleinberg
E356907
Jon Kleinberg is a prominent computer scientist known for his influential work in algorithms, networks, and data science, particularly in the analysis of large-scale social and information networks.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Jon Kleinberg canonical | 4 |
How this entity was disambiguated
This entity first appeared as the object of triple T3425620 — 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: Jon Kleinberg Context triple: [SIGKDD Innovation Award, notableRecipient, Jon Kleinberg]
-
A.
Daphne Koller
Daphne Koller is a computer scientist and entrepreneur best known as the co-founder of the online education platform Coursera and for her influential work in probabilistic graphical models and machine learning.
-
B.
Jeffrey D. Ullman
Jeffrey D. Ullman is a prominent American computer scientist known for his foundational contributions to database theory, algorithms, and formal languages, and for coauthoring several classic textbooks in computer science.
-
C.
Andrew G. Myers
Andrew G. Myers is an American organic chemist renowned for his contributions to complex molecule synthesis and medicinal chemistry.
-
D.
Michael P. Kearns
Michael P. Kearns is an American politician from New York who has served in various local and state offices, including roles in the New York State Assembly and Erie County government.
-
E.
Denny Vrandečić
Denny Vrandečić is a Croatian computer scientist and Wikimedian best known for founding Wikidata and proposing the Wikifunctions project.
- 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: Jon Kleinberg Target entity description: Jon Kleinberg is a prominent computer scientist known for his influential work in algorithms, networks, and data science, particularly in the analysis of large-scale social and information networks.
-
A.
Daphne Koller
Daphne Koller is a computer scientist and entrepreneur best known as the co-founder of the online education platform Coursera and for her influential work in probabilistic graphical models and machine learning.
-
B.
Jeffrey D. Ullman
Jeffrey D. Ullman is a prominent American computer scientist known for his foundational contributions to database theory, algorithms, and formal languages, and for coauthoring several classic textbooks in computer science.
-
C.
Andrew G. Myers
Andrew G. Myers is an American organic chemist renowned for his contributions to complex molecule synthesis and medicinal chemistry.
-
D.
Michael P. Kearns
Michael P. Kearns is an American politician from New York who has served in various local and state offices, including roles in the New York State Assembly and Erie County government.
-
E.
Denny Vrandečić
Denny Vrandečić is a Croatian computer scientist and Wikimedian best known for founding Wikidata and proposing the Wikifunctions project.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
academic
ⓘ
author ⓘ computer scientist ⓘ human ⓘ |
| authorOf |
Algorithm Design
ⓘ
Networks, Crowds, and Markets: Reasoning About a Highly Connected World ⓘ |
| awardReceived |
ACM Fellow
ⓘ
MacArthur Fellowship ⓘ National Academy of Engineering membership ⓘ National Academy of Sciences membership ⓘ
linked to:
National Academy of Sciences
Nevanlinna Prize ⓘ
linked to:
Rolf Nevanlinna Prize
Sloan Research Fellowship ⓘ
linked to:
Sloan Research Fellowships
|
| coAuthor |
David Easley
ⓘ
Éva Tardos ⓘ |
| educatedAt |
Cornell University
ⓘ
Massachusetts Institute of Technology ⓘ |
| employer | Cornell University ⓘ |
| familyName | Kleinberg ⓘ |
| fieldOfWork |
algorithmic game theory
ⓘ
algorithms ⓘ computer science ⓘ data science ⓘ graph algorithms ⓘ information networks ⓘ machine learning ⓘ network science ⓘ social network analysis ⓘ |
| givenName | Jon ⓘ |
| memberOf |
Association for Computing Machinery
ⓘ
National Academy of Engineering ⓘ National Academy of Sciences ⓘ |
| name | Jon Kleinberg ⓘ |
| notableWork |
HITS algorithm
ⓘ
work on algorithmic fairness and bias ⓘ work on algorithmic ranking and link analysis ⓘ work on clustering and community detection in networks ⓘ work on computational social science ⓘ work on network cascades and influence ⓘ work on small-world networks ⓘ work on temporal networks and bursty behavior ⓘ |
| positionHeld |
Professor of Computer Science at Cornell University
ⓘ
Tisch University Professor at Cornell University ⓘ
linked to:
Cornell University professorships
|
| researchInterest |
information diffusion
ⓘ
large-scale social networks ⓘ network dynamics ⓘ online algorithms ⓘ search in networks ⓘ |
| workLocation |
Ithaca, New York
ⓘ
linked to:
Ithaca, New York, United States
|
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: Jon Kleinberg Description of subject: Jon Kleinberg is a prominent computer scientist known for his influential work in algorithms, networks, and data science, particularly in the analysis of large-scale social and information networks.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.