This map shows the geographic impact of Timo Berthold'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 Timo Berthold with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timo Berthold more than expected).
This network shows the impact of papers produced by Timo Berthold. 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 Timo Berthold. The network helps show where Timo Berthold may publish in the future.
Co-authorship network of co-authors of Timo Berthold
This figure shows the co-authorship network connecting the top 25 collaborators of Timo Berthold.
A scholar is included among the top collaborators of Timo Berthold 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 Timo Berthold. Timo Berthold is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Berthold, Timo & Gregor Hendel. (2021). Learning To Scale Mixed-Integer Programs. Proceedings of the AAAI Conference on Artificial Intelligence. 35(5). 3661–3668.1 indexed citations
Shinano, Yuji, Tobias Achterberg, Timo Berthold, et al.. (2020). Solving Previously Unsolved MIP Instances with ParaSCIP on Supercomputers by using up to 80,000 Cores.1 indexed citations
Berthold, Timo, James Farmer, Stefan Heinz, & Michael Perregaard. (2017). Parallelization of the FICO Xpress-Optimizer. Optimization methods & software. 33(3). 518–529.8 indexed citations
10.
Ralphs, Ted K., Yuji Shinano, Timo Berthold, & Thorsten Koch. (2016). Parallel Solvers for Mixed Integer Linear Programming.7 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.