| Adversarial attacks on neural networks for graph data D Zügner, A Akbarnejad, S Günnemann Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 1109 | 2018 |
| Adversarial attacks on graph neural networks: Perturbations and their patterns D Zügner, O Borchert, A Akbarnejad, S Günnemann ACM Transactions on Knowledge Discovery from Data (TKDD) 14 (5), 1-31, 2020 | 673 | 2020 |
| Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann International conference on machine learning, 610-619, 2018 | 460 | 2018 |
| Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts B Charpentier, D Zügner, S Günnemann Neural Information Processing Systems, 2020 | 185 | 2020 |
| Certifiable robustness and robust training for graph convolutional networks D Zügner, S Günnemann Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 176 | 2019 |
| Language-Agnostic Representation Learning of Source Code from Structure and Context D Zügner, T Kirschstein, M Catasta, J Leskovec, S Günnemann International Conference on Learning Representations, 2021 | 143 | 2021 |
| Robustness of Graph Neural Networks at Scale S Geisler, T Schmidt, H Şirin, D Zügner, A Bojchevski, S Günnemann Neural Information Processing Systems, 2021 | 120 | 2021 |
| Reliable graph neural networks via robust aggregation S Geisler, D Zügner, S Günnemann Neural Information Processing Systems, 2020 | 82 | 2020 |
| Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification M Stadler, B Charpentier, S Geisler, D Zügner, S Günnemann Neural Information Processing Systems, 2021 | 77 | 2021 |
| Two for one: Diffusion models and force fields for coarse-grained molecular dynamics M Arts, V Garcia Satorras, CW Huang, D Zugner, M Federici, C Clementi, ... Journal of Chemical Theory and Computation 19 (18), 6151-6159, 2023 | 69 | 2023 |
| Certifiable robustness of graph convolutional networks under structure perturbations D Zügner, S Günnemann Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 66 | 2020 |
| Natural posterior network: Deep bayesian uncertainty for exponential family distributions B Charpentier, O Borchert, D Zügner, S Geisler, S Günnemann arXiv preprint arXiv:2105.04471, 2021 | 63 | 2021 |
| Mattergen: a generative model for inorganic materials design C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, S Shysheya, ... arXiv preprint arXiv:2312.03687, 2023 | 59 | 2023 |
| Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? AK Kopetzki, B Charpentier, D Zügner, S Giri, S Günnemann International Conference on Machine Learning 2021, 2020 | 49 | 2020 |
| Winning the lottery ahead of time: Efficient early network pruning J Rachwan, D Zügner, B Charpentier, S Geisler, M Ayle, S Günnemann International Conference on Machine Learning, 18293-18309, 2022 | 26 | 2022 |
| Group centrality maximization for large-scale graphs E Angriman, A van der Grinten, A Bojchevski, D Zügner, S Günnemann, ... 2020 Proceedings of the twenty-second workshop on Algorithm Engineering and …, 2020 | 22 | 2020 |
| Pushing the limits of RFID: Empowering RFID-based electronic article surveillance with data analytics techniques M Hauser, D Zügner, C Flath, F Thiesse | 20 | 2015 |
| Adversarial training for graph neural networks: Pitfalls, solutions, and new directions L Gosch, S Geisler, D Sturm, B Charpentier, D Zügner, S Günnemann Advances in Neural Information Processing Systems 36, 2024 | 18 | 2024 |
| On out-of-distribution detection with energy-based models S Elflein, B Charpentier, D Zügner, S Günnemann arXiv preprint arXiv:2107.08785, 2021 | 17 | 2021 |
| A study of joint graph inference and forecasting D Zügner, FX Aubet, VG Satorras, T Januschowski, S Günnemann, ... arXiv preprint arXiv:2109.04979, 2021 | 15 | 2021 |
