H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 98 Citations 103,639 228 World Ranking 151 National Ranking 91

Research.com Recognitions

Awards & Achievements

2014 - ACM AAAI Allen Newell Award For groundbreaking work in computer science in areas including social and information networks, information retrieval, and data science, and for bridging computing, economics and the social sciences.

2013 - ACM Fellow For contributions to the science of information and social networks.

2011 - Member of the National Academy of Sciences

2008 - ACM Prize in Computing For his contributions to the science of networks and the World Wide Web. His work is a deep combination of social insights and mathematical reasoning.

2008 - Member of the National Academy of Engineering For contributions to the understanding of the structure and behavior of the World Wide Web and other complex networks.

2007 - Fellow of the American Academy of Arts and Sciences

2006 - Rolf Nevanlinna Prize

2005 - Fellow of the MacArthur Foundation

1997 - Fellow of Alfred P. Sloan Foundation

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • The Internet

Jon Kleinberg mainly investigates Social network, World Wide Web, Theoretical computer science, Data science and Structure. The concepts of his Social network study are interwoven with issues in Information cascade, Data mining and Artificial intelligence. He has researched Artificial intelligence in several fields, including Simple and Dynamic network analysis.

As a member of one scientific family, Jon Kleinberg mostly works in the field of World Wide Web, focusing on Graph and, on occasion, Association rule learning, Connectivity and Directed graph. While the research belongs to areas of Theoretical computer science, he spends his time largely on the problem of Range, intersecting his research to questions surrounding Phenomenon and Network model. His Data science study integrates concerns from other disciplines, such as Scale, Presentation, Behavioral pattern, Interpersonal ties and Social media.

His most cited work include:

  • Authoritative sources in a hyperlinked environment (7452 citations)
  • Maximizing the spread of influence through a social network (4896 citations)
  • The link-prediction problem for social networks (2915 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Social network, Theoretical computer science, Artificial intelligence, Algorithm and Data science. His biological study spans a wide range of topics, including Data mining, Structure, Set, Social media and Dynamic network analysis. The subject of his Social media research is within the realm of World Wide Web.

His studies in Theoretical computer science integrate themes in fields like Graph and Graph. His work on Artificial intelligence is being expanded to include thematically relevant topics such as Machine learning. His research on Algorithm frequently links to adjacent areas such as Discrete mathematics.

He most often published in these fields:

  • Social network (14.57%)
  • Theoretical computer science (12.11%)
  • Artificial intelligence (10.99%)

What were the highlights of his more recent work (between 2015-2021)?

  • Artificial intelligence (10.99%)
  • Theoretical computer science (12.11%)
  • Machine learning (6.50%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Artificial intelligence, Theoretical computer science, Machine learning, Structure and Social media. His research integrates issues of Hypergraph, Graph and Graph in his study of Theoretical computer science. His Graph research integrates issues from Data mining and Pairwise comparison.

He works mostly in the field of Machine learning, limiting it down to concerns involving Medical imaging and, occasionally, Deep learning, Feature and Transfer of learning. Jon Kleinberg has included themes like Completeness, Randomness and TRACE in his Structure study. His Social media research includes themes of Information cascade and Social network.

Between 2015 and 2021, his most popular works were:

  • Inherent Trade-Offs in the Fair Determination of Risk Scores (455 citations)
  • On Fairness and Calibration (259 citations)
  • Human Decisions and Machine Predictions (226 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • The Internet

Jon Kleinberg mainly focuses on Artificial intelligence, Machine learning, Social network, Social media and Algorithm. His Artificial intelligence research incorporates elements of Recommender system and Scale. His Machine learning study incorporates themes from Inference and Medical imaging.

His Social network research includes elements of Weighted network, Microeconomics, Interpersonal ties and Computer network, Dynamic network analysis. His work deals with themes such as Cognitive psychology, Popularity and Information cascade, which intersect with Social media. His Data mining research is multidisciplinary, relying on both Graph and Pairwise comparison.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Authoritative sources in a hyperlinked environment

Jon M. Kleinberg.
Journal of the ACM (1999)

13360 Citations

Maximizing the spread of influence through a social network

David Kempe;Jon M. Kleinberg;Éva Tardos.
knowledge discovery and data mining (2003)

7631 Citations

The link-prediction problem for social networks

David Liben-Nowell;Jon Kleinberg.
Journal of the Association for Information Science and Technology (2007)

5301 Citations

Networks, Crowds, and Markets

David Easley;Jon Kleinberg.
Cambridge Books (2010)

4930 Citations

The small-world phenomenon: an algorithmic perspective

Jon Kleinberg.
symposium on the theory of computing (2000)

2770 Citations

Graphs over time: densification laws, shrinking diameters and possible explanations

Jure Leskovec;Jon Kleinberg;Christos Faloutsos.
knowledge discovery and data mining (2005)

2560 Citations

Graph evolution: Densification and shrinking diameters

Jure Leskovec;Jon Kleinberg;Christos Faloutsos.
ACM Transactions on Knowledge Discovery From Data (2007)

2297 Citations

Algorithm Design

Jon Kleinberg;Eva Tardos.
(2005)

2276 Citations

Bursty and Hierarchical Structure in Streams

Jon Kleinberg.
Data Mining and Knowledge Discovery (2003)

2235 Citations

Group formation in large social networks: membership, growth, and evolution

Lars Backstrom;Dan Huttenlocher;Jon Kleinberg;Xiangyang Lan.
knowledge discovery and data mining (2006)

2166 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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