Daniel B. Larremore
- Infectious Diseases top 1%
- Modeling and Simulation top 0.2%
- Statistical and Nonlinear Physics top 1%
- Biomedical Engineering top 10%
- Molecular Biology
- Co-authors
- Aaron ClausetRoy ParkerMichael J. MinaSamuel ArbesmanYonatan H. GradAllison C. MorganLeto PeelMarc Lipsitch
- Topics
- SARS-CoV-2 and COVID-19 Research (11 papers)Complex Network Analysis Techniques (11 papers)COVID-19 epidemiological studies (10 papers)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Daniel B. Larremore
49 papers receiving 3.8k citations
Hit Papers
Peers
Comparison fields: 5 of 191
- Infectious Diseases 1.4k
- Modeling and Simulation 755
- Statistical and Nonlinear Physics 745
- Biomedical Engineering 472
- Molecular Biology 443
Countries citing papers authored by Daniel B. Larremore
This map shows the geographic impact of Daniel B. Larremore'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 Daniel B. Larremore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel B. Larremore more than expected).
Fields of papers citing papers by Daniel B. Larremore
This network shows the impact of papers produced by Daniel B. Larremore. 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 Daniel B. Larremore. The network helps show where Daniel B. Larremore may publish in the future.
Co-authorship network of co-authors of Daniel B. Larremore
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel B. Larremore. A scholar is included among the top collaborators of Daniel B. Larremore 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 Daniel B. Larremore. Daniel B. Larremore 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 | Gender and retention patterns among U.S. facultybreakdown → | 71 |
| 4 | 1 | |
| 5 | 16 | |
| 6 | 6 | |
| 7 | Socioeconomic roots of academic facultybreakdown → | 87 |
| 8 | Quantifying hierarchy and dynamics in US faculty hiring and retentionbreakdown → | 100 |
| 9 | 24 | |
| 10 | 20 | |
| 11 | 18 | |
| 12 | 21 | |
| 13 | 77 | |
| 14 | 10 | |
| 15 | 4 | |
| 16 | Dynamics of beneficial epidemics | 8 |
| 17 | 110 | |
| 18 | Social Climber attachment in forming networks produces phase transition in "connectivity" | 1 |
| 19 | 47 | |
| 20 | 6 |
About Daniel B. Larremore
Daniel B. Larremore is a scholar working on Modeling and Simulation, Statistical and Nonlinear Physics and Gender Studies, having authored 53 papers that have together received 3.9k indexed citations. Recurring topics across this work include SARS-CoV-2 and COVID-19 Research (11 papers), Complex Network Analysis Techniques (11 papers) and COVID-19 epidemiological studies (10 papers). The work is most often cited by research in Modeling and Simulation (755 citations), Infectious Diseases (1.4k citations) and Statistical and Nonlinear Physics (745 citations). Daniel B. Larremore has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Aaron Clauset, Roy Parker, Michael J. Mina, Samuel Arbesman, Yonatan H. Grad, Allison C. Morgan, Leto Peel, Marc Lipsitch, Sarah Cobey and Kate M. Bubar. Their work appears in journals such as Nature, Science and New England Journal of Medicine.
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.