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
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 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.
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.
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.
Authoritative sources in a hyperlinked environment
Jon M. Kleinberg.
Journal of the ACM (1999)
Maximizing the spread of influence through a social network
David Kempe;Jon M. Kleinberg;Éva Tardos.
knowledge discovery and data mining (2003)
The link-prediction problem for social networks
David Liben-Nowell;Jon Kleinberg.
Journal of the Association for Information Science and Technology (2007)
Networks, Crowds, and Markets
David Easley;Jon Kleinberg.
Cambridge Books (2010)
The small-world phenomenon: an algorithmic perspective
symposium on the theory of computing (2000)
Graphs over time: densification laws, shrinking diameters and possible explanations
Jure Leskovec;Jon Kleinberg;Christos Faloutsos.
knowledge discovery and data mining (2005)
Graph evolution: Densification and shrinking diameters
Jure Leskovec;Jon Kleinberg;Christos Faloutsos.
ACM Transactions on Knowledge Discovery From Data (2007)
Jon Kleinberg;Eva Tardos.
Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery (2003)
Group formation in large social networks: membership, growth, and evolution
Lars Backstrom;Dan Huttenlocher;Jon Kleinberg;Xiangyang Lan.
knowledge discovery and data mining (2006)
Profile was last updated on December 6th, 2021.
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