Gregor Wiedemann

97 total papers · 1.6k total citations
25 papers, 806 citations indexed

About

Gregor Wiedemann is a scholar working on Artificial Intelligence, General Social Sciences and Sociology and Political Science. According to data from OpenAlex, Gregor Wiedemann has authored 25 papers receiving a total of 806 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 6 papers in General Social Sciences and 5 papers in Sociology and Political Science. Recurrent topics in Gregor Wiedemann's work include Computational and Text Analysis Methods (6 papers), Natural Language Processing Techniques (6 papers) and Topic Modeling (5 papers). Gregor Wiedemann is often cited by papers focused on Computational and Text Analysis Methods (6 papers), Natural Language Processing Techniques (6 papers) and Topic Modeling (5 papers). Gregor Wiedemann collaborates with scholars based in Germany, Switzerland and Belgium. Gregor Wiedemann's co-authors include Gerhard Heyer, Andreas Niekler, Daniel Maier, Hannah Schmid-Petri, Thomas Häußler, Silke Adam, Barbara Pfetsch, Ueli Reber, Annie Waldherr and Peter Miltner and has published in prestigious journals such as SHILAP Revista de lepidopterología, Cognitive Science and Psychological Research.

In The Last Decade

Gregor Wiedemann

20 papers receiving 755 citations

Hit Papers

Applying LDA Topic Modeli... 2018 2026 2020 2023 2018 100 200 300 400 500

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Gregor Wiedemann 295 290 244 220 49 25 806
Gerhard Heyer 273 0.9× 253 0.9× 437 1.8× 211 1.0× 105 2.1× 58 989
Annie Waldherr 343 1.2× 285 1.0× 202 0.8× 294 1.3× 53 1.1× 36 840
Hannah Schmid-Petri 421 1.4× 252 0.9× 157 0.6× 308 1.4× 36 0.7× 24 780
Ueli Reber 285 1.0× 264 0.9× 167 0.7× 223 1.0× 33 0.7× 15 723
Paul Nulty 340 1.2× 190 0.7× 261 1.1× 228 1.0× 41 0.8× 22 1.0k
Akitaka Matsuo 318 1.1× 185 0.6× 158 0.6× 174 0.8× 23 0.5× 11 847
Olessia Koltsova 246 0.8× 125 0.4× 350 1.4× 172 0.8× 99 2.0× 48 803
Cornelius Puschmann 429 1.5× 64 0.2× 167 0.7× 426 1.9× 106 2.2× 48 905
Mario Haim 560 1.9× 52 0.2× 197 0.8× 438 2.0× 76 1.6× 47 968
Loni Hagen 327 1.1× 58 0.2× 179 0.7× 243 1.1× 78 1.6× 42 733

Countries citing papers authored by Gregor Wiedemann

Since Specialization
Citations

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

Fields of papers citing papers by Gregor Wiedemann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gregor Wiedemann

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

All Works

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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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