Kaspar Riesen
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 2%
- Signal Processing top 5%
- Statistical and Nonlinear Physics top 5%
- Computational Theory and Mathematics top 5%
- Co-authors
- Horst BunkeAndreas FischerMiquel FerrerVolkmar FrinkenFrancesc SerratosaChing Y. SuenRolf IngoldErnest Valveny
- Topics
- Graph Theory and Algorithms (43 papers)Advanced Graph Neural Networks (30 papers)Advanced Image and Video Retrieval Techniques (14 papers)
- Journals
- Pattern RecognitionPattern Recognition LettersIEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
- Partner nations
- SwitzerlandSouth AfricaCanada
In The Last Decade
Kaspar Riesen
53 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 92
- Computer Vision and Pattern Recognition 945
- Artificial Intelligence 747
- Signal Processing 232
- Statistical and Nonlinear Physics 144
- Computational Theory and Mathematics 138
Countries citing papers authored by Kaspar Riesen
This map shows the geographic impact of Kaspar Riesen'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 Kaspar Riesen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaspar Riesen more than expected).
Fields of papers citing papers by Kaspar Riesen
This network shows the impact of papers produced by Kaspar Riesen. 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 Kaspar Riesen. The network helps show where Kaspar Riesen may publish in the future.
Co-authorship network of co-authors of Kaspar Riesen
This figure shows the co-authorship network connecting the top 25 collaborators of Kaspar Riesen. A scholar is included among the top collaborators of Kaspar Riesen 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 Kaspar Riesen. Kaspar Riesen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 7 | |
| 5 | 5 | |
| 6 | 25 | |
| 7 | 24 | |
| 8 | 44 | |
| 9 | 15 | |
| 10 | 1 | |
| 11 | 72 | |
| 12 | 31 | |
| 13 | 75 | |
| 14 | 26 | |
| 15 | 3 | |
| 16 | 47 | |
| 17 | Recent developments in graph classification and clustering using graph embedding kernels | 1 |
| 18 | 331 | |
| 19 | 3 | |
| 20 | Speeding up Graph Edit Distance Computation with a Bipartite Heuristic | 37 |
About Kaspar Riesen
Kaspar Riesen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 57 papers that have together received 1.3k indexed citations. Recurring topics across this work include Graph Theory and Algorithms (43 papers), Advanced Graph Neural Networks (30 papers) and Advanced Image and Video Retrieval Techniques (14 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (945 citations), Artificial Intelligence (747 citations) and Signal Processing (232 citations). Kaspar Riesen has collaborated with scholars based in Switzerland, South Africa and Canada. Frequent co-authors include Horst Bunke, Andreas Fischer, Miquel Ferrer, Volkmar Frinken, Francesc Serratosa, Ching Y. Suen, Rolf Ingold, Ernest Valveny, Michael Stauffer and Roman Schmidt. Their work appears in journals such as Pattern Recognition, Pattern Recognition Letters and IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics).
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.