Stephan Seufert
- Artificial Intelligence top 5%
- Computer Networks and Communications top 5%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Information Systems top 5%
- Co-authors
- Gerhard WeikumSrikanta BedathurMartin TheobaldAndrey GubichevSairam GurajadaIris MiliarakiJohannes HoffartDat Ba Nguyen
- Topics
- Graph Theory and Algorithms (5 papers)Advanced Graph Neural Networks (4 papers)Semantic Web and Ontologies (3 papers)
- Journals
- Max Planck Institute for Plasma PhysicsIEEE Data(base) Engineering Bulletin
In The Last Decade
Stephan Seufert
13 papers receiving 549 citations
Peers
Comparison fields: 5 of 63
- Artificial Intelligence 365
- Computer Networks and Communications 207
- Computer Vision and Pattern Recognition 175
- Signal Processing 156
- Information Systems 109
Countries citing papers authored by Stephan Seufert
This map shows the geographic impact of Stephan Seufert'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 Stephan Seufert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephan Seufert more than expected).
Fields of papers citing papers by Stephan Seufert
This network shows the impact of papers produced by Stephan Seufert. 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 Stephan Seufert. The network helps show where Stephan Seufert may publish in the future.
Co-authorship network of co-authors of Stephan Seufert
This figure shows the co-authorship network connecting the top 25 collaborators of Stephan Seufert. A scholar is included among the top collaborators of Stephan Seufert 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 Stephan Seufert. Stephan Seufert is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | Automatically Tracking Metadata and Provenance of Machine Learning Experiments | 44 |
| 3 | 5 | |
| 4 | 6 | |
| 5 | On Challenges in Machine Learning Model Management | 85 |
| 6 | 2 | |
| 7 | 105 | |
| 8 | 72 | |
| 9 | 20 | |
| 10 | 126 | |
| 11 | 6 | |
| 12 | 88 | |
| 13 | 23 |
About Stephan Seufert
Stephan Seufert is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 13 papers that have together received 593 indexed citations. Recurring topics across this work include Graph Theory and Algorithms (5 papers), Advanced Graph Neural Networks (4 papers) and Semantic Web and Ontologies (3 papers). The work is most often cited by research in Signal Processing (156 citations), Artificial Intelligence (365 citations) and Computer Networks and Communications (207 citations). Stephan Seufert has collaborated with scholars based in Germany, India and Belgium. Frequent co-authors include Gerhard Weikum, Srikanta Bedathur, Martin Theobald, Andrey Gubichev, Sairam Gurajada, Iris Miliaraki, Johannes Hoffart, Dat Ba Nguyen, Sebastian Schelter and Avishek Anand. Their work appears in journals such as Max Planck Institute for Plasma Physics and IEEE Data(base) Engineering Bulletin.
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.