Akshay Gadde
- Artificial Intelligence top 2%
- Statistical and Nonlinear Physics top 2%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 10%
- Computational Mechanics top 10%
- Co-authors
- Antonio OrtegaAamir AnisSunil K. NarangSam E. JohnSalman AvestimehrA. Salman AvestimehrPascal FrossardThomas Maugey
- Topics
- Complex Network Analysis Techniques (7 papers)Advanced Graph Neural Networks (7 papers)Machine Learning and Algorithms (3 papers)
- Journals
- IEEE Transactions on Signal ProcessingInfoscience (Ecole Polytechnique Fédérale de Lausanne)arXiv (Cornell University)
- Partner nations
- United StatesSwitzerlandChina
In The Last Decade
Akshay Gadde
12 papers receiving 751 citations
Peers
Comparison fields: 5 of 60
- Artificial Intelligence 612
- Statistical and Nonlinear Physics 387
- Computer Vision and Pattern Recognition 153
- Computer Networks and Communications 108
- Computational Mechanics 103
Countries citing papers authored by Akshay Gadde
This map shows the geographic impact of Akshay Gadde'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 Akshay Gadde with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akshay Gadde more than expected).
Fields of papers citing papers by Akshay Gadde
This network shows the impact of papers produced by Akshay Gadde. 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 Akshay Gadde. The network helps show where Akshay Gadde may publish in the future.
Co-authorship network of co-authors of Akshay Gadde
This figure shows the co-authorship network connecting the top 25 collaborators of Akshay Gadde. A scholar is included among the top collaborators of Akshay Gadde 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 Akshay Gadde. Akshay Gadde is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 4 | |
| 6 | 220 | |
| 7 | 51 | |
| 8 | 82 | |
| 9 | 136 | |
| 10 | 4 | |
| 11 | 72 | |
| 12 | 146 | |
| 13 | 47 |
About Akshay Gadde
Akshay Gadde is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 13 papers that have together received 770 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (7 papers), Advanced Graph Neural Networks (7 papers) and Machine Learning and Algorithms (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (387 citations), Artificial Intelligence (612 citations) and Computational Mathematics (6 citations). Akshay Gadde has collaborated with scholars based in United States, Switzerland and China. Frequent co-authors include Antonio Ortega, Aamir Anis, Sunil K. Narang, Sam E. John, Salman Avestimehr, A. Salman Avestimehr, Pascal Frossard, Thomas Maugey, Balu Adsumilli and Hassan Mansour. Their work appears in journals such as IEEE Transactions on Signal Processing, Infoscience (Ecole Polytechnique Fédérale de Lausanne) and arXiv (Cornell University).
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.