Tyll Krueger
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
- Modeling and Simulation top 10%
- COVID-19 epidemiological studies
Papers in
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- Complex Network Analysis Techniques 11
- Opinion Dynamics and Social Influence 8
- Co-authors
- Janusz Szwabiński (1 shared paper)Michael Barber (1 shared paper)Andreas Krueger (1 shared paper)Niloy Ganguly (6 shared papers)S. Saha (3 shared papers)Marcin Bodych (2 shared papers)Animesh Mukherjee (4 shared papers)Krzysztof Gogolewski (1 shared paper)
In The Last Decade
Tyll Krueger
17 papers receiving 154 citations
Peers
Comparison fields: 5 of 55
- Statistical and Nonlinear Physics 92
- Modeling and Simulation 33
- Communication 17
- Tourism, Leisure and Hospitality Management 2
- Health 11
Countries citing papers authored by Tyll Krueger
This map shows the geographic impact of Tyll Krueger'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 Tyll Krueger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tyll Krueger more than expected).
Fields of papers citing papers by Tyll Krueger
This network shows the impact of papers produced by Tyll Krueger. 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 Tyll Krueger. The network helps show where Tyll Krueger may publish in the future.
Co-authors
The 25 scholars most cited alongside Tyll Krueger, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 39 | |
| 2 | 2022 | 37 | |
| 3 | 2006 | 36 | |
| 4 | 2016 | 12 | |
| 5 | 2014 | 9 | |
| 6 | 2014 | 6 | |
| 7 | 2005 | 6 | |
| 8 | 2012 | 3 | |
| 9 | 2024 | 3 | |
| 10 | 2019 | 2 | |
| 11 | 2013 | 2 | |
| 12 | Lyapunov exponents and transport in Self-Organized Criticality | 2000 | 1 |
| 13 | Entropy and Algorithmic Complexity in Quantum Information Theory: a Quantum Brudno's Theorem | 2005 | 1 |
| 14 | 2018 | 1 | |
| 15 | 2011 | 1 | |
| 16 | 2016 | 1 | |
| 17 | 2024 | 1 | |
| 18 | 2025 | 0 | |
| 19 | Overview of MIMIC Phase I Material/Device Correlation Study | 1991 | 0 |
About Tyll Krueger
Tyll Krueger is a scholar working on Statistical and Nonlinear Physics, Molecular Biology, Computer Networks and Communications, Condensed Matter Physics and Mathematical Physics, having authored 19 papers that have together received 161 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (11 papers), Opinion Dynamics and Social Influence (8 papers), Stochastic processes and statistical mechanics (3 papers), Peer-to-Peer Network Technologies (3 papers), COVID-19 epidemiological studies (3 papers), Theoretical and Computational Physics (3 papers), Social Media and Politics (2 papers) and Mathematical and Theoretical Epidemiology and Ecology Models (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (92 citations), Modeling and Simulation (33 citations), Communication (17 citations), Tourism, Leisure and Hospitality Management (2 citations) and Health (11 citations). Tyll Krueger has collaborated with scholars based in Poland, India and Germany. Frequent co-authors include Janusz Szwabiński, Michael Barber, Andreas Krueger, Niloy Ganguly, S. Saha, Marcin Bodych, Animesh Mukherjee, Krzysztof Gogolewski, Anna Gambin and Ewa Szczurek. Their work appears in journals such as IEEE Journal on Selected Areas in Communications, Advances in Complex Systems, Journal of Theoretical Biology, IEEE Transactions on Network Science and Engineering and Europhysics Letters (EPL).
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.