Oliver Eberle
- Artificial Intelligence top 10%
- Materials Chemistry
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Signal Processing
- Co-authors
- Grégoire MontavonShinichi NakajimaJonas LedererKristof T. SchüttKlaus-Robert MüllerKlaus‐Robert MüllerMatteo VallerianiStephanie Brandl
- Topics
- Explainable Artificial Intelligence (XAI) (4 papers)Topic Modeling (4 papers)Time Series Analysis and Forecasting (3 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceScience AdvancesResearch at the University of Copenhagen (University of Copenhagen)
- Partner nations
- GermanySouth KoreaIsrael
In The Last Decade
Oliver Eberle
9 papers receiving 239 citations
Peers
Comparison fields: 5 of 57
- Artificial Intelligence 174
- Materials Chemistry 34
- Computer Vision and Pattern Recognition 31
- Computational Theory and Mathematics 24
- Signal Processing 17
Countries citing papers authored by Oliver Eberle
This map shows the geographic impact of Oliver Eberle'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 Oliver Eberle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Oliver Eberle more than expected).
Fields of papers citing papers by Oliver Eberle
This network shows the impact of papers produced by Oliver Eberle. 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 Oliver Eberle. The network helps show where Oliver Eberle may publish in the future.
Co-authorship network of co-authors of Oliver Eberle
This figure shows the co-authorship network connecting the top 25 collaborators of Oliver Eberle. A scholar is included among the top collaborators of Oliver Eberle 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 Oliver Eberle. Oliver Eberle is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 5 | |
| 7 | 11 | |
| 8 | 4 | |
| 9 | 155 | |
| 10 | XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks | 15 |
| 11 | 43 |
About Oliver Eberle
Oliver Eberle is a scholar working on General Social Sciences, Signal Processing and Artificial Intelligence, having authored 11 papers that have together received 241 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (4 papers), Topic Modeling (4 papers) and Time Series Analysis and Forecasting (3 papers). The work is most often cited by research in Artificial Intelligence (174 citations), Health Informatics (6 citations) and Information Systems and Management (11 citations). Oliver Eberle has collaborated with scholars based in Germany, South Korea and Israel. Frequent co-authors include Grégoire Montavon, Shinichi Nakajima, Jonas Lederer, Kristof T. Schütt, Klaus-Robert Müller, Klaus‐Robert Müller, Matteo Valleriani, Stephanie Brandl, Anders Søgaard and Ilias Chalkidis. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Science Advances and Research at the University of Copenhagen (University of Copenhagen).
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.