Kamesh Madduri

201 total papers · 3.1k total citations
69 papers, 1.5k citations indexed

About

Kamesh Madduri is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Kamesh Madduri has authored 69 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Vision and Pattern Recognition, 23 papers in Artificial Intelligence and 21 papers in Computer Networks and Communications. Recurrent topics in Kamesh Madduri's work include Graph Theory and Algorithms (27 papers), Complex Network Analysis Techniques (14 papers) and Advanced Graph Neural Networks (13 papers). Kamesh Madduri is often cited by papers focused on Graph Theory and Algorithms (27 papers), Complex Network Analysis Techniques (14 papers) and Advanced Graph Neural Networks (13 papers). Kamesh Madduri collaborates with scholars based in United States, South Korea and Switzerland. Kamesh Madduri's co-authors include David A. Bader, George M. Slota, Aydın Buluç, Sivasankaran Rajamanickam, Humayun Kabir, Daniel Chavarría-Miranda, Karl Jiang, David Ediger, Leonid Oliker and Samuel Williams and has published in prestigious journals such as Blood, The Journal of Physical Chemistry B and IEEE Transactions on Intelligent Transportation Systems.

In The Last Decade

Kamesh Madduri

66 papers receiving 1.4k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Kamesh Madduri 675 628 454 393 359 69 1.5k
Julian Shun 841 1.2× 1.1k 1.8× 824 1.8× 701 1.8× 206 0.6× 64 1.8k
Md. Mostofa Ali Patwary 352 0.5× 461 0.7× 541 1.2× 229 0.6× 167 0.5× 28 1.1k
Dongdai Lin 348 0.5× 398 0.6× 981 2.2× 141 0.4× 183 0.5× 175 1.6k
Ken A. Hawick 413 0.6× 164 0.3× 180 0.4× 213 0.5× 197 0.5× 159 1.2k
Hank Childs 616 0.9× 632 1.0× 199 0.4× 214 0.5× 79 0.2× 108 1.6k
Prasanna Balaprakash 340 0.5× 125 0.2× 483 1.1× 256 0.7× 315 0.9× 115 1.6k
M. Vento 346 0.5× 906 1.4× 740 1.6× 108 0.3× 123 0.3× 24 1.7k
Assefaw H. Gebremedhin 303 0.4× 237 0.4× 294 0.6× 121 0.3× 173 0.5× 74 1.2k
Venkatram Vishwanath 923 1.4× 237 0.4× 254 0.6× 534 1.4× 49 0.1× 123 1.8k
Cevdet Aykanat 1.1k 1.6× 303 0.5× 373 0.8× 659 1.7× 66 0.2× 107 1.8k

Countries citing papers authored by Kamesh Madduri

Since Specialization
Citations

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

Fields of papers citing papers by Kamesh Madduri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kamesh Madduri

This figure shows the co-authorship network connecting the top 25 collaborators of Kamesh Madduri. A scholar is included among the top collaborators of Kamesh Madduri 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 Kamesh Madduri. Kamesh Madduri is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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