Jonas Lederer
Impact in
- Artificial Intelligence top 10%
- Explainable Artificial Intelligence (XAI)
- Advanced Graph Neural Networks
- Adversarial Robustness in Machine Learning
- Topic Modeling
- Machine Learning in Healthcare
-
- Computational Drug Discovery Methods
Papers in
-
- Machine Learning in Materials Science 7
- Electronic and Structural Properties of Oxides 1
-
- Explainable Artificial Intelligence (XAI) 4
- Advanced Graph Neural Networks 2
- Topic Modeling 2
- Co-authors
- Kristof T. Schütt (4 shared papers)Grégoire Montavon (5 shared papers)Shinichi Nakajima (3 shared papers)Oliver Eberle (2 shared papers)Klaus-Robert Müller (2 shared papers)Klaus‐Robert Müller (4 shared papers)Michael Gastegger (2 shared papers)Waldemar Kaiser (1 shared paper)
- Journals
- Information Fusion (1 paper)IEEE Signal Processing Magazine (1 paper)Nature Communications (1 paper)Advanced Theory and Simulations (1 paper)Physical Chemistry Chemical Physics (1 paper)
- Partner nations
- GermanySouth KoreaUnited States
In The Last Decade
Jonas Lederer
9 papers receiving 336 citations
Peers
Comparison fields: 5 of 72
- Artificial Intelligence 166
- Computational Theory and Mathematics 57
- Health Informatics 4
- Materials Chemistry 129
- Information Systems and Management 12
Countries citing papers authored by Jonas Lederer
This map shows the geographic impact of Jonas Lederer'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 Jonas Lederer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonas Lederer more than expected).
Fields of papers citing papers by Jonas Lederer
This network shows the impact of papers produced by Jonas Lederer. 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 Jonas Lederer. The network helps show where Jonas Lederer may publish in the future.
Co-authors
The 20 scholars most cited alongside Jonas Lederer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 155 | |
| 2 | 2022 | 56 | |
| 3 | 2023 | 55 | |
| 4 | 2018 | 44 | |
| 5 | XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks | 2020 | 15 |
| 6 | 2023 | 7 | |
| 7 | 2025 | 6 | |
| 8 | 2025 | 2 | |
| 9 | 2025 | 1 |
About Jonas Lederer
Jonas Lederer is a scholar working on Materials Chemistry, Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology and Information Systems and Management, having authored 9 papers that have together received 341 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (7 papers), Explainable Artificial Intelligence (XAI) (4 papers), Advanced Graph Neural Networks (2 papers), Computational Drug Discovery Methods (2 papers), Topic Modeling (2 papers), Molecular Junctions and Nanostructures (1 paper), Metabolomics and Mass Spectrometry Studies (1 paper) and Electronic and Structural Properties of Oxides (1 paper). The work is most often cited by research in Artificial Intelligence (166 citations), Computational Theory and Mathematics (57 citations), Health Informatics (4 citations), Materials Chemistry (129 citations) and Information Systems and Management (12 citations). Jonas Lederer has collaborated with scholars based in Germany, South Korea and United States. Frequent co-authors include Kristof T. Schütt, Grégoire Montavon, Shinichi Nakajima, Oliver Eberle, Klaus-Robert Müller, Klaus‐Robert Müller, Michael Gastegger, Waldemar Kaiser, Alessio Gagliardi and Niklas W. A. Gebauer. Their work appears in journals such as Information Fusion, IEEE Signal Processing Magazine, Nature Communications, Advanced Theory and Simulations and Physical Chemistry Chemical Physics.
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.