Pushing the limits of RFID: Empowering RFID-based Electronic Article Surveillance with Data Analytics Techniques. - Matthias Hauser; Daniel Zügner; Christoph Flath; Frédéric Thiesse (ICIS, 2015)

NetGAN: Generating Graphs via Random Walks. - Aleksandar Bojchevski; Oleksandr Shchur; Daniel Zügner; Stephan Günnemann (ICML, 2018)

Adversarial Attacks on Neural Networks for Graph Data. - Daniel Zügner; Amir Akbarnejad; Stephan Günnemann (KDD, 2018)

Adversarial Attacks on Graph Neural Networks. - Daniel Zügner; Amir Akbarnejad; Stephan Günnemann (GI-Jahrestagung, 2019)

Adversarial Attacks on Graph Neural Networks via Meta Learning. - Daniel Zügner; Stephan Günnemann (ICLR (Poster), 2019)

Certifiable Robustness and Robust Training for Graph Convolutional Networks. - Daniel Zügner; Stephan Günnemann (KDD, 2019)

Oktoberfest Food Dataset. - Alexander Ziller; Julius Hansjakob; Vitalii Rusinov; Daniel Zügner; Peter Vogel; Stephan Günnemann (CoRR, 2019)

Group Centrality Maximization for Large-scale Graphs. - Eugenio Angriman; Alexander van der Grinten; Aleksandar Bojchevski; Daniel Zügner; Stephan Günnemann; Henning Meyerhenke (ALENEX, 2020)

Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations. - Daniel Zügner; Stephan Günnemann (KDD, 2020)

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. - Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (NeurIPS, 2020)

Reliable Graph Neural Networks via Robust Aggregation. - Simon Geisler; Daniel Zügner; Stephan Günnemann (NeurIPS, 2020)

Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns. - Daniel Zügner; Oliver Borchert; Amir Akbarnejad; Stephan Günnemann (ACM Trans. Knowl. Discov. Data, 2020)

Language-Agnostic Representation Learning of Source Code from Structure and Context. - Daniel Zügner; Tobias Kirschstein; Michele Catasta; Jure Leskovec; Stephan Günnemann (ICLR, 2021)

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? - Anna-Kathrin Kopetzki; Bertrand Charpentier; Daniel Zügner; Sandhya Giri; Stephan Günnemann (ICML, 2021)

Robustness of Graph Neural Networks at Scale. - Simon Geisler; Tobias Schmidt; Hakan Sirin; Daniel Zügner; Aleksandar Bojchevski; Stephan Günnemann (NeurIPS, 2021)

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. - Maximilian Stadler; Bertrand Charpentier; Simon Geisler; Daniel Zügner; Stephan Günnemann (NeurIPS, 2021)

On Out-of-distribution Detection with Energy-based Models. - Sven Elflein; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (CoRR, 2021)

A Study of Joint Graph Inference and Forecasting. - Daniel Zügner; François-Xavier Aubet; Victor Garcia Satorras; Tim Januschowski; Stephan Günnemann; Jan Gasthaus (CoRR, 2021)

Monte Carlo EM for Deep Time Series Anomaly Detection. - François-Xavier Aubet; Daniel Zügner; Jan Gasthaus (CoRR, 2021)

Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. - Bertrand Charpentier; Oliver Borchert; Daniel Zügner; Simon Geisler; Stephan Günnemann (ICLR, 2022)

End-to-End Learning of Probabilistic Hierarchies on Graphs. - Daniel Zügner; Bertrand Charpentier; Morgane Ayle; Sascha Geringer; Stephan Günnemann (ICLR, 2022)

Winning the Lottery Ahead of Time: Efficient Early Network Pruning. - John Rachwan; Daniel Zügner; Bertrand Charpentier; Simon Geisler; Morgane Ayle; Stephan Günnemann (ICML, 2022)

On the Robustness and Anomaly Detection of Sparse Neural Networks. - Morgane Ayle; Bertrand Charpentier; John Rachwan; Daniel Zügner; Simon Geisler; Stephan Günnemann (CoRR, 2022)

Adversarial Robustness of Graph Neural Networks. - Daniel Zügner (2022)

Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions. - Lukas Gosch; Simon Geisler; Daniel Sturm 002; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (NeurIPS, 2023)

Training Differentially Private Graph Neural Networks with Random Walk Sampling. - Morgane Ayle; Jan Schuchardt; Lukas Gosch; Daniel Zügner; Stephan Günnemann (CoRR, 2023)

Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. - Marloes Arts; Victor Garcia Satorras; Chin-Wei Huang; Daniel Zügner; Marco Federici; Cecilia Clementi; Frank Noé; Robert Pinsler; Rianne van den Berg (CoRR, 2023)

Adversarial Training for Graph Neural Networks. - Lukas Gosch; Simon Geisler; Daniel Sturm 002; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (CoRR, 2023)

MatterGen: a generative model for inorganic materials design. - Claudio Zeni; Robert Pinsler; Daniel Zügner; Andrew Fowler; Matthew Horton; Xiang Fu 005; Sasha Shysheya; Jonathan Crabbé; Lixin Sun; Jake Smith; Ryota Tomioka; Tian Xie 002 (CoRR, 2023)

Expected Probabilistic Hierarchies. - Marcel Kollovieh; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (NeurIPS, 2024)

A generative model for inorganic materials design. - Claudio Zeni; Robert Pinsler; Daniel Zügner; Andrew Fowler; Matthew Horton; Xiang Fu 005; Zilong Wang; Aliaksandra Shysheya; Jonathan Crabbé; Shoko Ueda; Roberto Sordillo; Lixin Sun; et al. (Nat., 2025)