Deepak Agarwal

180 papers receiving 4.5k citations

Deepak Agarwal's Hit Papers

Regression-based latent factor models 2009 · 385 citations
3850+6+12Years since publication100200300400500

Peers

Deepak Agarwal
Comparison fields: 5 of 186
  • Fluid Flow and Transfer Processes 859
  • Information Systems 1.2k
  • Urology 219
  • Computational Mathematics 22
  • Management Science and Operations Research 433
Replace Ming Yang with:
Ming Yang China
Hao Ying United States
Rong Zheng Canada
Nader Meskin Qatar
Kelvin K. L. Wong Australia
Haralambos Sarimveis Greece
Mukesh Singhal United States
Munir Ahmad Pakistan
Apu Kumar Saha India
John McCall United Kingdom
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Countries citing papers authored by Deepak Agarwal

Since Specialization
Citations

This map shows the geographic impact of Deepak Agarwal'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 Deepak Agarwal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepak Agarwal more than expected).

Fields of papers citing papers by Deepak Agarwal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Deepak Agarwal. 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 Deepak Agarwal. The network helps show where Deepak Agarwal may publish in the future.

Co-authors

The 25 scholars most cited alongside Deepak Agarwal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Deepak Agarwal Line = papers co-authored together Deepak Agarwal links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 192 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Performance and emissions characteristics of Jatropha oil (preheated and blends) in a direct injection compression ignition engine
Hit paper breakdown →
2007520
2
Regression-based latent factor models
Hit paper breakdown →
2009385
3 2007305
4 2006278
5 2010206
6 2018172
7 2011116
8 201599
9 201998
10 200994
11 200984
12 201676
13 198668
14 201067
15 200461
16 200658
17 201157
18 200656
19 200155
20 201654

About Deepak Agarwal

Deepak Agarwal is a scholar working on Pulmonary and Respiratory Medicine, Surgery, Information Systems, Artificial Intelligence and Urology, having authored 192 papers that have together received 4.9k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (23 papers), Kidney Stones and Urolithiasis Treatments (21 papers), Urinary Bladder and Prostate Research (18 papers), Advanced Bandit Algorithms Research (15 papers), Gallbladder and Bile Duct Disorders (14 papers), Pediatric Urology and Nephrology Studies (10 papers), Biodiesel Production and Applications (8 papers) and Data Management and Algorithms (8 papers). The work is most often cited by research in Fluid Flow and Transfer Processes (859 citations), Information Systems (1.2k citations), Urology (219 citations), Computational Mathematics (22 citations) and Management Science and Operations Research (433 citations). Deepak Agarwal has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Avinash Kumar Ágarwal, Bee-Chung Chen, Shailendra Sinha, Pradheep Elango, Amy E. Krambeck, Marcelino Rivera, Vivek A. Saraswat, Tim Large, G. Choudhuri and Radha K. Dhiman. Their work appears in journals such as Journal of Endourology, Urology, World Journal of Urology, Journal of Energy Resources Technology and The Journal of Urology.

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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