H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 65 Citations 19,945 201 World Ranking 1169 National Ranking 64

Research.com Recognitions

Awards & Achievements

2020 - ACM Fellow For contributions to data summarization and privacy enabling data management and analysis

2013 - ACM Distinguished Member

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Artificial intelligence
  • Algorithm

His primary areas of investigation include Data mining, Data stream mining, Theoretical computer science, Data stream and Algorithm. In the field of Data mining, his study on Aggregate overlaps with subjects such as Focus. His work carried out in the field of Data stream mining brings together such families of science as Zipf's law, Distributed algorithm, Cluster analysis, Range and Data set.

His Theoretical computer science study combines topics in areas such as Uncertain data, Probabilistic logic and Graph. The various areas that Graham Cormode examines in his Data stream study include Skewness and Data warehouse. His Algorithm research is multidisciplinary, relying on both Entropy and Wireless sensor network.

His most cited work include:

  • An improved data stream summary: the count-min sketch and its applications (1373 citations)
  • Advances and Open Problems in Federated Learning (571 citations)
  • What's hot and what's not: tracking most frequent items dynamically (532 citations)

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

His primary areas of study are Data mining, Theoretical computer science, Data stream mining, Data stream and Algorithm. His work in the fields of Uncertain data overlaps with other areas such as Tuple. His work on Communication complexity is typically connected to Streaming algorithm as part of general Theoretical computer science study, connecting several disciplines of science.

His biological study spans a wide range of topics, including Distributed computing, Wireless sensor network, Quantile, Approximation algorithm and Automatic summarization. While working in this field, Graham Cormode studies both Data stream and Point. His Algorithm study incorporates themes from Range and Hash function, Count–min sketch.

He most often published in these fields:

  • Data mining (28.82%)
  • Theoretical computer science (25.69%)
  • Data stream mining (23.96%)

What were the highlights of his more recent work (between 2017-2021)?

  • Differential privacy (7.29%)
  • Combinatorics (9.72%)
  • Streaming algorithm (10.76%)

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

Differential privacy, Combinatorics, Streaming algorithm, Algorithm and Discrete mathematics are his primary areas of study. His Differential privacy research also works with subjects such as

  • Range query and Protocol most often made with reference to News aggregator,
  • Synthetic data together with Aggregate and Mechanism design. The concepts of his Combinatorics study are interwoven with issues in Quantile and Extreme value theory.

He has included themes like Range, Hash function and Graph in his Algorithm study. His work focuses on many connections between Graph and other disciplines, such as Data set, that overlap with his field of interest in Data stream mining. His Data stream mining research is within the category of Data mining.

Between 2017 and 2021, his most popular works were:

  • Advances and Open Problems in Federated Learning (571 citations)
  • Marginal Release Under Local Differential Privacy (55 citations)
  • Privacy at Scale: Local Differential Privacy in Practice (52 citations)

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

  • Statistics
  • Artificial intelligence
  • The Internet

Graham Cormode mainly investigates Differential privacy, Combinatorics, Streaming algorithm, Data collection and Federated learning. Differential privacy is a subfield of Data mining that he investigates. When carried out as part of a general Combinatorics research project, his work on Pointer jumping, Maximal independent set, Independent set and Vertex is frequently linked to work in Quadratic equation, therefore connecting diverse disciplines of study.

His Pointer jumping research includes themes of Vertex model and Tilde. His Data collection research incorporates themes from Key, Scale and Internet privacy. His Federated learning research is multidisciplinary, incorporating elements of Training set, World Wide Web, Mobile device, Service provider and Orchestration.

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

An improved data stream summary: the count-min sketch and its applications

Graham Cormode;S. Muthukrishnan.
Journal of Algorithms (2005)

1530 Citations

Key differences between Web 1.0 and Web 2.0

Graham Cormode;Balachander Krishnamurthy.
First Monday (2008)

1219 Citations

What's hot and what's not: tracking most frequent items dynamically

Graham Cormode;S. Muthukrishnan.
symposium on principles of database systems (2003)

808 Citations

Advances and Open Problems in Federated Learning

Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet.
Foundations and Trends® in Machine Learning (2021)

603 Citations

Advances and Open Problems in Federated Learning

Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet.
arXiv: Learning (2019)

585 Citations

Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches

Graham Cormode;Minos Garofalakis;Peter J. Haas;Chris Jermaine.
(2012)

401 Citations

Node Classification in Social Networks

Smriti Bhagat;Graham Cormode;S. Muthukrishnan.
Social Network Data Analytics (2011)

348 Citations

Differentially Private Spatial Decompositions

Graham Cormode;Cecilia Procopiuc;Divesh Srivastava;Entong Shen.
international conference on data engineering (2012)

327 Citations

Finding frequent items in data streams

Graham Cormode;Marios Hadjieleftheriou.
very large data bases (2008)

325 Citations

Anonymizing bipartite graph data using safe groupings

Graham Cormode;Divesh Srivastava;Ting Yu;Qing Zhang.
very large data bases (2008)

298 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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