H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 78 Citations 44,424 230 World Ranking 501 National Ranking 300

Research.com Recognitions

Awards & Achievements

2020 - ACM Fellow For contributions to web science modeling, analytics, and algorithms

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Algorithm
  • Artificial intelligence

His main research concerns World Wide Web, Theoretical computer science, Rank, Data mining and Graph. His World Wide Web research is multidisciplinary, incorporating elements of Structure, Graph and Giant component. His Theoretical computer science research is multidisciplinary, relying on both Cryptography, Directed graph, Exploit, Coding and Error detection and correction.

His research in Rank intersects with topics in Ranking, Context, Ranking and Nearest neighbor search. His Data mining study incorporates themes from Web search query, Web query classification, Information retrieval and Clustering high-dimensional data. The Graph study combines topics in areas such as Streaming algorithm and Biological network.

His most cited work include:

  • Graph structure in the Web (2491 citations)
  • Pig latin: a not-so-foreign language for data processing (1762 citations)
  • Rank aggregation methods for the Web (1551 citations)

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

The scientist’s investigation covers issues in Theoretical computer science, Combinatorics, Discrete mathematics, World Wide Web and Algorithm. The concepts of his Theoretical computer science study are interwoven with issues in Graph, Data mining, Directed graph, Graph and Voltage graph. His Combinatorics study combines topics from a wide range of disciplines, such as Upper and lower bounds and Omega.

His Discrete mathematics research is multidisciplinary, incorporating perspectives in Class and Metric. His work investigates the relationship between World Wide Web and topics such as Information retrieval that intersect with problems in Set. His study in Approximation algorithm is interdisciplinary in nature, drawing from both Matrix and Cluster analysis.

He most often published in these fields:

  • Theoretical computer science (22.74%)
  • Combinatorics (19.13%)
  • Discrete mathematics (15.52%)

What were the highlights of his more recent work (between 2016-2021)?

  • Cluster analysis (7.58%)
  • Theoretical computer science (22.74%)
  • Combinatorics (19.13%)

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

Ravi Kumar mostly deals with Cluster analysis, Theoretical computer science, Combinatorics, Upper and lower bounds and Algorithm. His Cluster analysis study integrates concerns from other disciplines, such as Efficient algorithm, Point and Approximation algorithm. He combines Theoretical computer science and Baseline in his studies.

Ravi Kumar interconnects Value, Simple and Tight closure in the investigation of issues within Combinatorics. His Upper and lower bounds research incorporates themes from Omega, Data access, Treewidth, Binary number and Differential privacy. His research integrates issues of Sample, Markov process and Aggregate in his study of Algorithm.

Between 2016 and 2021, his most popular works were:

  • Fair Clustering Through Fairlets (141 citations)
  • Counting Graphlets: Space vs Time (50 citations)
  • Local search methods for k-means with outliers (45 citations)

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

  • Statistics
  • Algorithm
  • Artificial intelligence

His primary scientific interests are in Theoretical computer science, Cluster analysis, Algorithm, Combinatorics and Approximation algorithm. He integrates many fields in his works, including Theoretical computer science and Online setting. His research in the fields of CURE data clustering algorithm overlaps with other disciplines such as Line.

Ravi Kumar has included themes like Conductance, Low-rank approximation, Rank and Discrete mathematics in his Algorithm study. His study in the fields of Binary logarithm under the domain of Combinatorics overlaps with other disciplines such as AKA. His study in Approximation algorithm is interdisciplinary in nature, drawing from both Center, Value, Representation and Series.

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

Graph structure in the Web

Andrei Broder;Ravi Kumar;Farzin Maghoul;Prabhakar Raghavan.
the web conference (2000)

4286 Citations

Pig latin: a not-so-foreign language for data processing

Christopher Olston;Benjamin Reed;Utkarsh Srivastava;Ravi Kumar.
international conference on management of data (2008)

2450 Citations

Rank aggregation methods for the Web

Cynthia Dwork;Ravi Kumar;Moni Naor;D. Sivakumar.
the web conference (2001)

2053 Citations

Structure and Evolution of Online Social Networks.

Ravi Kumar;Jasmine Novak;Andrew Tomkins.
Link Mining (2010)

1934 Citations

Propagation of trust and distrust

R. Guha;Ravi Kumar;Prabhakar Raghavan;Andrew Tomkins.
the web conference (2004)

1851 Citations

Trawling the Web for emerging cyber-communities

Ravi Kumar;Prabhakar Raghavan;Sridhar Rajagopalan;Andrew Tomkins.
the web conference (1999)

1516 Citations

The web as a graph: measurements, models, and methods

Jon M. Kleinberg;Ravi Kumar;Prabhakar Raghavan;Sridhar Rajagopalan.
computing and combinatorics conference (1999)

1379 Citations

Comparing top k lists

Ronald Fagin;Ravi Kumar;D. Sivakumar.
symposium on discrete algorithms (2003)

1219 Citations

Geographic routing in social networks

David Liben-Nowell;Jasmine Novak;Ravi Kumar;Prabhakar Raghavan.
Proceedings of the National Academy of Sciences of the United States of America (2005)

999 Citations

Stochastic models for the Web graph

R. Kumar;P. Raghavan;S. Rajagopalan;D. Sivakumar.
foundations of computer science (2000)

956 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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