Gesine Reinert

3.5k total citations
92 papers, 1.8k citations indexed

About

Gesine Reinert is a scholar working on Molecular Biology, Statistics and Probability and Statistical and Nonlinear Physics. According to data from OpenAlex, Gesine Reinert has authored 92 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 25 papers in Statistics and Probability and 22 papers in Statistical and Nonlinear Physics. Recurrent topics in Gesine Reinert's work include Random Matrices and Applications (22 papers), Complex Network Analysis Techniques (22 papers) and Bioinformatics and Genomic Networks (19 papers). Gesine Reinert is often cited by papers focused on Random Matrices and Applications (22 papers), Complex Network Analysis Techniques (22 papers) and Bioinformatics and Genomic Networks (19 papers). Gesine Reinert collaborates with scholars based in United Kingdom, United States and China. Gesine Reinert's co-authors include Michael S. Waterman, Fengzhu Sun, Sophie Schbath, Charlotte M. Deane, Michael Newman, Adrian Röllin, Giovanni Peccati, Jie Ren, Larry Goldstein and A. D. Barbour and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Gesine Reinert

84 papers receiving 1.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gesine Reinert United Kingdom 24 880 420 317 307 224 92 1.8k
Richard Arratia United States 26 712 0.8× 992 2.4× 652 2.1× 141 0.5× 821 3.7× 52 2.5k
David Levin United States 9 134 0.2× 443 1.1× 587 1.9× 325 1.1× 644 2.9× 37 2.0k
Iddo Eliazar Israel 24 507 0.6× 152 0.4× 148 0.5× 941 3.1× 415 1.9× 163 2.1k
Michel Benaı̈m France 23 90 0.1× 238 0.6× 197 0.6× 276 0.9× 323 1.4× 66 1.7k
P. K. Pollett Australia 22 144 0.2× 122 0.3× 453 1.4× 137 0.4× 479 2.1× 141 1.5k
Anton Wakolbinger Germany 15 249 0.3× 204 0.5× 171 0.5× 133 0.4× 627 2.8× 67 2.0k
Jiashun Jin United States 23 456 0.5× 627 1.5× 701 2.2× 297 1.0× 23 0.1× 51 1.9k
J. D. Biggins United Kingdom 27 208 0.2× 485 1.2× 959 3.0× 141 0.5× 1.3k 5.9× 67 2.9k
Maury Bramson United States 33 248 0.3× 101 0.2× 537 1.7× 514 1.7× 1.4k 6.4× 78 3.3k
S. L. Zabell United States 21 98 0.1× 425 1.0× 364 1.1× 144 0.5× 126 0.6× 59 1.3k

Countries citing papers authored by Gesine Reinert

Since Specialization
Citations

This map shows the geographic impact of Gesine Reinert'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 Gesine Reinert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gesine Reinert more than expected).

Fields of papers citing papers by Gesine Reinert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gesine Reinert. 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 Gesine Reinert. The network helps show where Gesine Reinert may publish in the future.

Co-authorship network of co-authors of Gesine Reinert

This figure shows the co-authorship network connecting the top 25 collaborators of Gesine Reinert. A scholar is included among the top collaborators of Gesine Reinert 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 Gesine Reinert. Gesine Reinert is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Reinert, Gesine, et al.. (2024). Simulating Weak Attacks in a New Duplication–Divergence Model with Node Loss. Entropy. 26(10). 813–813.
2.
Cucuringu, Mihai, et al.. (2023). The GNAR-edge model: a network autoregressive model for networks with time-varying edge weights. Journal of Complex Networks. 11(6). 3 indexed citations
3.
Beguerisse-Díaz, Mariano, et al.. (2022). Extracting Information from Gene Coexpression Networks of Rhizobium leguminosarum. Journal of Computational Biology. 29(7). 752–768. 2 indexed citations
4.
Beguerisse-Díaz, Mariano, et al.. (2021). Robust gene coexpression networks using signed distance correlation. Bioinformatics. 37(14). 1982–1989. 10 indexed citations
5.
Briol, François‐Xavier, Robert E. Gaunt, Jackson Gorham, et al.. (2021). Stein's Method Meets Statistics: A Review of Some Recent Developments. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 3 indexed citations
6.
Reinert, Gesine, et al.. (2021). A Stein Goodness-of-test for Exponential Random Graph Models.. Oxford University Research Archive (ORA) (University of Oxford). 415–423. 1 indexed citations
7.
Reinert, Gesine, et al.. (2020). COGENT: evaluating the consistency of gene co-expression networks. Bioinformatics. 37(13). 1928–1929. 5 indexed citations
8.
Klimm, Florian, Charlotte M. Deane, & Gesine Reinert. (2020). Hypergraphs for predicting essential genes using multiprotein complex data. Journal of Complex Networks. 9(2). 24 indexed citations
9.
Riolo, Maria A., George T. Cantwell, Gesine Reinert, & M. E. J. Newman. (2017). Efficient method for estimating the number of communities in a network. Physical review. E. 96(3). 32310–32310. 49 indexed citations
10.
Gaunt, Robert E., et al.. (2016). Poisson approximation of subgraph counts in stochastic block models and\n a graphon model. Springer Link (Chiba Institute of Technology). 3 indexed citations
11.
Rito, Tiago, Charlotte M. Deane, & Gesine Reinert. (2012). The Importance of Age and High Degree, in Protein-Protein Interaction Networks. Journal of Computational Biology. 19(6). 785–795. 10 indexed citations
12.
Reinert, Gesine, et al.. (2012). Normal and Compound Poisson Approximations for Pattern Occurrences in NGS Reads. Journal of Computational Biology. 19(6). 839–854. 5 indexed citations
13.
Wan, Lin, Gesine Reinert, Fengzhu Sun, & Michael S. Waterman. (2010). Alignment-Free Sequence Comparison (II): Theoretical Power of Comparison Statistics. Journal of Computational Biology. 17(11). 1467–1490. 76 indexed citations
14.
Reinert, Gesine & Adrian Röllin. (2010). Random subgraph counts and U-statistics: multivariate normal approximation via exchangeable pairs and embedding. Journal of Applied Probability. 47(2). 378–393. 3 indexed citations
15.
Reinert, Gesine, et al.. (2009). Alignment-Free Sequence Comparison (I): Statistics and Power. Journal of Computational Biology. 16(12). 1615–1634. 141 indexed citations
16.
Peccati, Giovanni, et al.. (2009). Second order Poincaré inequalities and CLTs on Wiener space. Journal of Functional Analysis. 257(2). 593–609. 35 indexed citations
17.
Onnela, Jukka‐Pekka, et al.. (2008). Sampling bias due to structural heterogeneity and limited internal diffusion. arXiv (Cornell University). 1 indexed citations
18.
Johnson, Neil F., et al.. (2008). Bias in Epidemiological Studies of Conflict Mortality. Journal of Peace Research. 45(5). 653–663. 23 indexed citations
19.
Barbour, A. D. & Gesine Reinert. (2000). Small worlds. arXiv (Cornell University). 1 indexed citations
20.
Barbour, A. D., Russell Gerrard, & Gesine Reinert. (2000). Iterates of expanding maps. Probability Theory and Related Fields. 116(2). 151–180. 13 indexed citations

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.

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