Kankan Sarkar
- Modeling and Simulation top 0.5%
- Public Health, Environmental and Occupational Health top 5%
- Genetics top 10%
- Infectious Diseases top 10%
- Economics and Econometrics top 10%
- Co-authors
- Subhas KhajanchiJuan J. NietoJayanta MondalKottakkaran Sooppy NisarSayed F. AbdelwahabS G AparnaD. ShankarN. Srinivasa Rao
- Topics
- Mathematical and Theoretical Epidemiology and Ecology Models (9 papers)COVID-19 epidemiological studies (5 papers)Evolution and Genetic Dynamics (5 papers)
- Cited by
- Modeling and SimulationPublic Health, Environmental and Occupational HealthInfectious Diseases
- Partner nations
- IndiaSpainSaudi Arabia
In The Last Decade
Kankan Sarkar
13 papers receiving 725 citations
Peers
Comparison fields: 5 of 80
- Modeling and Simulation 451
- Public Health, Environmental and Occupational Health 430
- Genetics 211
- Infectious Diseases 184
- Economics and Econometrics 102
Countries citing papers authored by Kankan Sarkar
This map shows the geographic impact of Kankan Sarkar'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 Kankan Sarkar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kankan Sarkar more than expected).
Fields of papers citing papers by Kankan Sarkar
This network shows the impact of papers produced by Kankan Sarkar. 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 Kankan Sarkar. The network helps show where Kankan Sarkar may publish in the future.
Co-authorship network of co-authors of Kankan Sarkar
This figure shows the co-authorship network connecting the top 25 collaborators of Kankan Sarkar. A scholar is included among the top collaborators of Kankan Sarkar 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 Kankan Sarkar. Kankan Sarkar 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 | 29 | |
| 3 | 15 | |
| 4 | 42 | |
| 5 | 27 | |
| 6 | 17 | |
| 7 | 98 | |
| 8 | 298 | |
| 9 | 138 | |
| 10 | 31 | |
| 11 | 11 | |
| 12 | 15 | |
| 13 | 24 |
About Kankan Sarkar
Kankan Sarkar is a scholar working on Modeling and Simulation, Public Health, Environmental and Occupational Health and Infectious Diseases, having authored 13 papers that have together received 746 indexed citations. Recurring topics across this work include Mathematical and Theoretical Epidemiology and Ecology Models (9 papers), COVID-19 epidemiological studies (5 papers) and Evolution and Genetic Dynamics (5 papers). The work is most often cited by research in Modeling and Simulation (451 citations), Public Health, Environmental and Occupational Health (430 citations) and Infectious Diseases (184 citations). Kankan Sarkar has collaborated with scholars based in India, Spain and Saudi Arabia. Frequent co-authors include Subhas Khajanchi, Juan J. Nieto, Jayanta Mondal, Kottakkaran Sooppy Nisar, Sayed F. Abdelwahab, S G Aparna, D. Shankar, N. Srinivasa Rao, V. V. S. S. Sarma and M. S. Krishna. Their work appears in journals such as Journal of the Franklin Institute, Chaos Solitons & Fractals and Journal of Marine Systems.
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.