Amit Puniyani
- Computer Networks and Communications top 2%
- Statistical and Nonlinear Physics top 2%
- Information Systems top 10%
- Artificial Intelligence
- Molecular Biology
- Co-authors
- Rajan M. LukoseLada A. AdamicBernardo A. HubermanMarcus W. FeldmanUri LibermanRohan MukherjeeVinit GhoshYang Liu
- Topics
- Evolution and Genetic Dynamics (3 papers)Genetic Mapping and Diversity in Plants and Animals (1 paper)Peer-to-Peer Network Technologies (1 paper)
- Journals
- Theoretical Population BiologyJournal of Global Information ManagementPhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics
- Partner nations
- United StatesIndiaIsrael
In The Last Decade
Amit Puniyani
5 papers receiving 735 citations
Hit Papers
Peers
Comparison fields: 5 of 73
- Computer Networks and Communications 459
- Statistical and Nonlinear Physics 423
- Information Systems 92
- Artificial Intelligence 74
- Molecular Biology 65
Countries citing papers authored by Amit Puniyani
This map shows the geographic impact of Amit Puniyani'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 Amit Puniyani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amit Puniyani more than expected).
Fields of papers citing papers by Amit Puniyani
This network shows the impact of papers produced by Amit Puniyani. 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 Amit Puniyani. The network helps show where Amit Puniyani may publish in the future.
Co-authorship network of co-authors of Amit Puniyani
This figure shows the co-authorship network connecting the top 25 collaborators of Amit Puniyani. A scholar is included among the top collaborators of Amit Puniyani 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 Amit Puniyani. Amit Puniyani 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 | 1 | |
| 3 | 8 | |
| 4 | 2 | |
| 5 | 8 | |
| 6 | Search in power-law networksbreakdown → | 770 |
About Amit Puniyani
Amit Puniyani is a scholar working on Genetics, Organizational Behavior and Human Resource Management and Signal Processing, having authored 6 papers that have together received 789 indexed citations. Recurring topics across this work include Evolution and Genetic Dynamics (3 papers), Genetic Mapping and Diversity in Plants and Animals (1 paper) and Peer-to-Peer Network Technologies (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (423 citations), Computer Networks and Communications (459 citations) and Signal Processing (46 citations). Amit Puniyani has collaborated with scholars based in United States, India and Israel. Frequent co-authors include Rajan M. Lukose, Lada A. Adamic, Bernardo A. Huberman, Marcus W. Feldman, Uri Liberman, Rohan Mukherjee, Vinit Ghosh and Yang Liu. Their work appears in journals such as Theoretical Population Biology, Journal of Global Information Management and Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
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.