Nils M. Kriege
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Computational Theory and Mathematics top 5%
- Molecular Biology
- Statistical and Nonlinear Physics top 10%
- Co-authors
- Christopher MorrisPetra MutzelFredrik JohanssonKarsten KleinOliver KochLina HumbeckJonathan MasciKristian Kersting
- Topics
- Advanced Graph Neural Networks (9 papers)Graph Theory and Algorithms (7 papers)Complex Network Analysis Techniques (5 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionStatistical and Nonlinear Physics
- Partner nations
- GermanyAustriaUnited Kingdom
In The Last Decade
Nils M. Kriege
22 papers receiving 450 citations
Peers
Comparison fields: 5 of 90
- Artificial Intelligence 270
- Computer Vision and Pattern Recognition 162
- Computational Theory and Mathematics 108
- Molecular Biology 91
- Statistical and Nonlinear Physics 91
Countries citing papers authored by Nils M. Kriege
This map shows the geographic impact of Nils M. Kriege'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 Nils M. Kriege with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nils M. Kriege more than expected).
Fields of papers citing papers by Nils M. Kriege
This network shows the impact of papers produced by Nils M. Kriege. 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 Nils M. Kriege. The network helps show where Nils M. Kriege may publish in the future.
Co-authorship network of co-authors of Nils M. Kriege
This figure shows the co-authorship network connecting the top 25 collaborators of Nils M. Kriege. A scholar is included among the top collaborators of Nils M. Kriege 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 Nils M. Kriege. Nils M. Kriege is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 8 | |
| 3 | 4 | |
| 4 | 0 | |
| 5 | 202 | |
| 6 | 5 | |
| 7 | 25 | |
| 8 | 3 | |
| 9 | 0 | |
| 10 | 1 | |
| 11 | Recognizing Cuneiform Signs Using Graph Based Methods | 1 |
| 12 | 2 | |
| 13 | A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels. | 11 |
| 14 | 46 | |
| 15 | On Valid Optimal Assignment Kernels and Applications to Graph Classification | 3 |
| 16 | 12 | |
| 17 | 7 | |
| 18 | 17 | |
| 19 | 1 | |
| 20 | 1 |
About Nils M. Kriege
Nils M. Kriege is a scholar working on Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Physical and Theoretical Chemistry, having authored 24 papers that have together received 469 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (9 papers), Graph Theory and Algorithms (7 papers) and Complex Network Analysis Techniques (5 papers). The work is most often cited by research in Artificial Intelligence (270 citations), Computer Vision and Pattern Recognition (162 citations) and Statistical and Nonlinear Physics (91 citations). Nils M. Kriege has collaborated with scholars based in Germany, Austria and United Kingdom. Frequent co-authors include Christopher Morris, Petra Mutzel, Fredrik Johansson, Karsten Klein, Oliver Koch, Lina Humbeck, Jonathan Masci, Kristian Kersting, Matthias Fey and Jan Eric Lenssen. Their work appears in journals such as Nature Communications, Data Mining and Knowledge Discovery and Lecture notes in computer science.
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.