Nikhil Ketkar

2.9k total citations · 1 hit paper
14 papers, 626 citations indexed

About

Nikhil Ketkar is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Nikhil Ketkar has authored 14 papers receiving a total of 626 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 5 papers in Information Systems and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Nikhil Ketkar's work include Data Mining Algorithms and Applications (5 papers), Data Management and Algorithms (3 papers) and Artificial Intelligence in Games (3 papers). Nikhil Ketkar is often cited by papers focused on Data Mining Algorithms and Applications (5 papers), Data Management and Algorithms (3 papers) and Artificial Intelligence in Games (3 papers). Nikhil Ketkar collaborates with scholars based in United States. Nikhil Ketkar's co-authors include Jojo Moolayil, Lawrence B. Holder, Diane J. Cook, G. Michael Youngblood, Jeffrey P. Jones, Timothy J. Carlson and James C. Tilton and has published in prestigious journals such as Journal of Medicinal Chemistry, ACM SIGKDD Explorations Newsletter and The Florida AI Research Society.

In The Last Decade

Nikhil Ketkar

14 papers receiving 603 citations

Hit Papers

Deep Learning with Python 2017 2026 2020 2023 2017 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep Learning with Python
    2017 378 citations Nikhil Ketkar Apress eBooks profile →

Peers

Nikhil Ketkar
Xuming Han China
Nikolay Nagornov Russia
André Luis Debiaso Rossi Brazil
Jeff Heaton United States
Cheng Tan China
Xingjian Li China
Qiong Chen China
Lipo Wang Singapore
Xuming Han China
Nikhil Ketkar
Citations per year, relative to Nikhil Ketkar Nikhil Ketkar (= 1×) peers Xuming Han

Countries citing papers authored by Nikhil Ketkar

Since Specialization
Citations

This map shows the geographic impact of Nikhil Ketkar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nikhil Ketkar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nikhil Ketkar more than expected).

Fields of papers citing papers by Nikhil Ketkar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nikhil Ketkar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nikhil Ketkar. The network helps show where Nikhil Ketkar may publish in the future.

Co-authorship network of co-authors of Nikhil Ketkar

This figure shows the co-authorship network connecting the top 25 collaborators of Nikhil Ketkar. A scholar is included among the top collaborators of Nikhil Ketkar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nikhil Ketkar. Nikhil Ketkar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
1.
Ketkar, Nikhil & Jojo Moolayil. (2021). Deep Learning with Python. Apress eBooks. 90 indexed citations
2.
Moolayil, Jojo & Nikhil Ketkar. (2021). Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch. 27 indexed citations
3.
Ketkar, Nikhil. (2017). Deep Learning with Python. Apress eBooks. 378 indexed citations breakdown →
4.
Youngblood, G. Michael, et al.. (2010). Using Intelligent Agents to Build Navigation Meshes. The Florida AI Research Society. 1 indexed citations
5.
Ketkar, Nikhil & G. Michael Youngblood. (2010). A Largest Common Subsequence-based Distance Measure for Classifying Player Motion Traces in Virtual Worlds. The Florida AI Research Society. 2 indexed citations
6.
Youngblood, G. Michael, et al.. (2010). Representational complexity of reactive agents. 257–264. 9 indexed citations
7.
Ketkar, Nikhil, Lawrence B. Holder, & Diane J. Cook. (2009). Faster computation of the direct product kernel for graph classification. 148. 267–274. 1 indexed citations
8.
Ketkar, Nikhil, Lawrence B. Holder, & Diane J. Cook. (2009). Empirical comparison of graph classification algorithms. 148. 259–266. 16 indexed citations
9.
Tilton, James C., Diane J. Cook, & Nikhil Ketkar. (2008). The Integraton of Graph-Based Knowledge Discovery with Image Segmentation Hierarchies for Data Analysis, Data Mining and Knowledge Discovery. 66. III – 491. 3 indexed citations
10.
Ketkar, Nikhil, et al.. (2008). High Confidence Predictions of Drug−Drug Interactions: Predicting Affinities for Cytochrome P450 2C9 with Multiple Computational Methods. Journal of Medicinal Chemistry. 51(3). 648–654. 20 indexed citations
11.
Ketkar, Nikhil, Lawrence B. Holder, & Diane J. Cook. (2006). Mining in the Proximity of Subgraphs. 7 indexed citations
12.
Ketkar, Nikhil, Lawrence B. Holder, & Diane J. Cook. (2005). Comparison of graph-based and logic-based multi-relational data mining. ACM SIGKDD Explorations Newsletter. 7(2). 64–71. 14 indexed citations
13.
Ketkar, Nikhil, Lawrence B. Holder, & Diane J. Cook. (2005). Subdue. 71–76. 57 indexed citations
14.
Ketkar, Nikhil, Lawrence B. Holder, & Diane J. Cook. (2005). Qualitative comparison of graph-based and logic-based multi-relational data mining. 25–32. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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