Florian Klimm
Impact in
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- Complex Network Analysis Techniques
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- Topological and Geometric Data Analysis
Papers in
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- Mitochondrial Function and Pathology 4
- Bioinformatics and Genomic Networks 2
- Single-cell and spatial transcriptomics 2
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- Complex Network Analysis Techniques 5
- Co-authors
- Peter J. Mucha (3 shared papers)Danielle S. Bassett (3 shared papers)Jean M. Carlson (1 shared paper)Miroslav Kramár (2 shared papers)Mason A. Porter (2 shared papers)Heather A. Harrington (2 shared papers)Konstantin Mischaikow (2 shared papers)Dane Taylor (2 shared papers)
- Journals
- Nature Communications (2 papers)PLoS Computational Biology (2 papers)Journal of Complex Networks (2 papers)Cell (1 paper)SIAM Journal on Applied Mathematics (1 paper)
- Partner nations
- United KingdomGermanyUnited States
In The Last Decade
Florian Klimm
13 papers receiving 297 citations
Peers
Comparison fields: 5 of 75
- Statistical and Nonlinear Physics 77
- Computational Theory and Mathematics 81
- Clinical Biochemistry 28
- Cognitive Neuroscience 74
- Aging 4
Countries citing papers authored by Florian Klimm
This map shows the geographic impact of Florian Klimm'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 Florian Klimm with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Florian Klimm more than expected).
Fields of papers citing papers by Florian Klimm
This network shows the impact of papers produced by Florian Klimm. 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 Florian Klimm. The network helps show where Florian Klimm may publish in the future.
Co-authors
The 25 scholars most cited alongside Florian Klimm, 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 | 2015 | 91 | |
| 2 | 2014 | 63 | |
| 3 | 2023 | 26 | |
| 4 | 2021 | 25 | |
| 5 | 2020 | 24 | |
| 6 | 2014 | 16 | |
| 7 | 2020 | 15 | |
| 8 | 2018 | 14 | |
| 9 | 2016 | 11 | |
| 10 | 2021 | 8 | |
| 11 | 2025 | 7 | |
| 12 | Complex contagions on noisy geometric networks. | 2014 | 1 |
| 13 | 2022 | 1 | |
| 14 | 2022 | 0 |
About Florian Klimm
Florian Klimm is a scholar working on Molecular Biology, Statistical and Nonlinear Physics, Computational Theory and Mathematics, Cognitive Neuroscience and Clinical Biochemistry, having authored 14 papers that have together received 302 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (5 papers), Mitochondrial Function and Pathology (4 papers), Topological and Geometric Data Analysis (3 papers), Metabolism and Genetic Disorders (2 papers), Bioinformatics and Genomic Networks (2 papers), Single-cell and spatial transcriptomics (2 papers), Functional Brain Connectivity Studies (2 papers) and Artificial Immune Systems Applications (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (77 citations), Computational Theory and Mathematics (81 citations), Clinical Biochemistry (28 citations), Cognitive Neuroscience (74 citations) and Aging (4 citations). Florian Klimm has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Peter J. Mucha, Danielle S. Bassett, Jean M. Carlson, Miroslav Kramár, Mason A. Porter, Heather A. Harrington, Konstantin Mischaikow, Dane Taylor, Gesine Reinert and Charlotte M. Deane. Their work appears in journals such as Nature Communications, PLoS Computational Biology, Journal of Complex Networks, Cell and SIAM Journal on Applied Mathematics.
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.