Madhav Jha
- Artificial Intelligence top 10%
- Statistical and Nonlinear Physics top 5%
- Computational Theory and Mathematics top 5%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications top 10%
- Co-authors
- C. SeshadhriAli PınarSofya RaskhodnikovaL. C. W. DixonSumit GuptaPallav GuptaVijay ChaudharyDavid P. Woodruff
- Topics
- Complexity and Algorithms in Graphs (7 papers)Complex Network Analysis Techniques (4 papers)Cryptography and Data Security (4 papers)
- Cited by
- Statistical and Nonlinear PhysicsComputational Theory and MathematicsArtificial Intelligence
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Madhav Jha
16 papers receiving 284 citations
Peers
Comparison fields: 5 of 48
- Artificial Intelligence 150
- Statistical and Nonlinear Physics 136
- Computational Theory and Mathematics 100
- Computer Vision and Pattern Recognition 74
- Computer Networks and Communications 66
Countries citing papers authored by Madhav Jha
This map shows the geographic impact of Madhav Jha'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 Madhav Jha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Madhav Jha more than expected).
Fields of papers citing papers by Madhav Jha
This network shows the impact of papers produced by Madhav Jha. 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 Madhav Jha. The network helps show where Madhav Jha may publish in the future.
Co-authorship network of co-authors of Madhav Jha
This figure shows the co-authorship network connecting the top 25 collaborators of Madhav Jha. A scholar is included among the top collaborators of Madhav Jha 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 Madhav Jha. Madhav Jha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 11 | |
| 3 | 13 | |
| 4 | 4 | |
| 5 | 2 | |
| 6 | 7 | |
| 7 | 70 | |
| 8 | 2 | |
| 9 | 1 | |
| 10 | 37 | |
| 11 | When a Graph is not so Simple: Counting Triangles in Multigraph Streams. | 3 |
| 12 | 78 | |
| 13 | 20 | |
| 14 | From the Birthday Paradox to a Practical Sublinear Space Streaming Algorithm for Triangle Counting | 2 |
| 15 | 9 | |
| 16 | 15 | |
| 17 | 19 |
About Madhav Jha
Madhav Jha is a scholar working on Computational Theory and Mathematics, Numerical Analysis and Statistical and Nonlinear Physics, having authored 17 papers that have together received 294 indexed citations. Recurring topics across this work include Complexity and Algorithms in Graphs (7 papers), Complex Network Analysis Techniques (4 papers) and Cryptography and Data Security (4 papers). The work is most often cited by research in Statistical and Nonlinear Physics (136 citations), Computational Theory and Mathematics (100 citations) and Artificial Intelligence (150 citations). Madhav Jha has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include C. Seshadhri, Ali Pınar, Sofya Raskhodnikova, Ali Pınar, L. C. W. Dixon, Sumit Gupta, Pallav Gupta, Vijay Chaudhary, David P. Woodruff and Elena Grigorescu. Their work appears in journals such as SIAM Journal on Computing, Journal of Optimization Theory and Applications and Algorithmica.
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.