Daniel Zügner
Impact in
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Adversarial Robustness in Machine Learning
- Topic Modeling
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- Complex Network Analysis Techniques
Papers in
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- Adversarial Robustness in Machine Learning 7
- Advanced Graph Neural Networks 5
- Anomaly Detection Techniques and Applications 2
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- Complex Network Analysis Techniques 2
- Co-authors
- Stephan Günnemann (9 shared papers)Amir Akbarnejad (4 shared papers)Oliver Borchert (1 shared paper)Michele Catasta (1 shared paper)Jure Leskovec (1 shared paper)Tobias Kirschstein (1 shared paper)Marco Orsini Federici (1 shared paper)Chin‐Wei Huang (1 shared paper)
- Journals
- ACM Transactions on Knowledge Discovery from Data (1 paper)Journal of Chemical Theory and Computation (1 paper)Gesellschaft für Informatik (GI) (1 paper)International Conference on Machine Learning (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- GermanyNetherlandsUnited Kingdom
In The Last Decade
Daniel Zügner
11 papers receiving 376 citations
Peers
Comparison fields: 5 of 61
- Artificial Intelligence 285
- Statistical and Nonlinear Physics 68
- Computational Mathematics 2
- Signal Processing 35
- Software 12
Countries citing papers authored by Daniel Zügner
This map shows the geographic impact of Daniel Zügner'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 Daniel Zügner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Zügner more than expected).
Fields of papers citing papers by Daniel Zügner
This network shows the impact of papers produced by Daniel Zügner. 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 Daniel Zügner. The network helps show where Daniel Zügner may publish in the future.
Co-authors
The 19 scholars most cited alongside Daniel Zügner, 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 | 2019 | 169 | |
| 2 | 2020 | 62 | |
| 3 | 2023 | 54 | |
| 4 | 2021 | 42 | |
| 5 | 2020 | 24 | |
| 6 | NetGAN: Generating Graphs via Random Walks | 2018 | 13 |
| 7 | Pushing the limits of RFID: Empowering RFID-based Electronic Article Surveillance with Data Analytics Techniques | 2015 | 11 |
| 8 | Adversarial Attacks on Classification Models for Graphs | 2018 | 5 |
| 9 | Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts | 2020 | 3 |
| 10 | Reliable Graph Neural Networks via Robust Aggregation | 2020 | 2 |
| 11 | 2019 | 2 | |
| 12 | 2023 | 0 |
About Daniel Zügner
Daniel Zügner is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Electrical and Electronic Engineering, Molecular Biology and Social Psychology, having authored 12 papers that have together received 387 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (7 papers), Advanced Graph Neural Networks (5 papers), Anomaly Detection Techniques and Applications (2 papers), Complex Network Analysis Techniques (2 papers), Ferroelectric and Negative Capacitance Devices (1 paper), RFID technology advancements (1 paper), Mental Health via Writing (1 paper) and Indoor and Outdoor Localization Technologies (1 paper). The work is most often cited by research in Artificial Intelligence (285 citations), Statistical and Nonlinear Physics (68 citations), Computational Mathematics (2 citations), Signal Processing (35 citations) and Software (12 citations). Daniel Zügner has collaborated with scholars based in Germany, Netherlands and United Kingdom. Frequent co-authors include Stephan Günnemann, Amir Akbarnejad, Oliver Borchert, Michele Catasta, Jure Leskovec, Tobias Kirschstein, Marco Orsini Federici, Chin‐Wei Huang, Frank Noé and Robert Pinsler. Their work appears in journals such as ACM Transactions on Knowledge Discovery from Data, Journal of Chemical Theory and Computation, Gesellschaft für Informatik (GI), International Conference on Machine Learning and arXiv (Cornell University).
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.