Anup Rao
- Artificial Intelligence top 5%
- Statistical and Nonlinear Physics top 5%
- Computational Theory and Mathematics top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Co-authors
- Ryan A. RossiNesreen K. AhmedTung MaiDanai KoutraSungchul KimEunyee KohNedim LipkaJiong Zhu
- Topics
- Advanced Graph Neural Networks (7 papers)Complex Network Analysis Techniques (6 papers)Recommender Systems and Techniques (5 papers)
- Journals
- SIAM Journal on ComputingInformation Processing LettersarXiv (Cornell University)
- Partner nations
- United StatesIndiaAustralia
In The Last Decade
Anup Rao
25 papers receiving 398 citations
Peers
Comparison fields: 5 of 57
- Artificial Intelligence 272
- Statistical and Nonlinear Physics 140
- Computational Theory and Mathematics 95
- Computer Vision and Pattern Recognition 84
- Information Systems 69
Countries citing papers authored by Anup Rao
This map shows the geographic impact of Anup Rao'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 Anup Rao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anup Rao more than expected).
Fields of papers citing papers by Anup Rao
This network shows the impact of papers produced by Anup Rao. 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 Anup Rao. The network helps show where Anup Rao may publish in the future.
Co-authorship network of co-authors of Anup Rao
This figure shows the co-authorship network connecting the top 25 collaborators of Anup Rao. A scholar is included among the top collaborators of Anup Rao 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 Anup Rao. Anup Rao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 6 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 10 | |
| 6 | 1 | |
| 7 | 2 | |
| 8 | 126 | |
| 9 | 2 | |
| 10 | 25 | |
| 11 | 4 | |
| 12 | On Densification for Minwise Hashing. | 3 |
| 13 | 5 | |
| 14 | 64 | |
| 15 | 10 | |
| 16 | 23 | |
| 17 | Algorithms for Lipschitz Learning on Graphs | 7 |
| 18 | Fast, provable algorithms for Isotonic regression in all ℓ- p -norms | 9 |
| 19 | 50 | |
| 20 | 27 |
About Anup Rao
Anup Rao is a scholar working on Computational Mathematics, Statistics and Probability and Artificial Intelligence, having authored 28 papers that have together received 413 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (7 papers), Complex Network Analysis Techniques (6 papers) and Recommender Systems and Techniques (5 papers). The work is most often cited by research in Statistical and Nonlinear Physics (140 citations), Artificial Intelligence (272 citations) and Computational Mathematics (4 citations). Anup Rao has collaborated with scholars based in United States, India and Australia. Frequent co-authors include Ryan A. Rossi, Nesreen K. Ahmed, Tung Mai, Danai Koutra, Sungchul Kim, Eunyee Koh, Nedim Lipka, Jiong Zhu, Rasmus Kyng and C. Pandu Rangan. Their work appears in journals such as SIAM Journal on Computing, Information Processing Letters 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.