| Predict then Propagate: Graph Neural Networks meet Personalized PageRank J Klicpera, A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2019 | 1629* | 2019 |
| Pitfalls of graph neural network evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann Relational Representation Learning, NeurIPS 2018 Workshop, 2018 | 1151 | 2018 |
| Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR) 2018, 2018 | 676 | 2018 |
| NetGAN: Generating Graphs via Random Walks A Bojchevski, O Shchur, D Zügner, S Günnemann International Conference on Machine Learning (ICML), 610-619, 2018 | 415 | 2018 |
| Adversarial Attacks on Node Embeddings via Graph Poisoning A Bojchevski, S Günnemann International Conference on Machine Learning (ICML), 695-704, 2019 | 327 | 2019 |
| Scaling graph neural networks with approximate pagerank A Bojchevski, J Gasteiger, B Perozzi, A Kapoor, M Blais, B Rózemberczki, ... Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 261 | 2020 |
| Certifiable Robustness to Graph Perturbations A Bojchevski, S Günnemann Advances in Neural Information Processing Systems (NeurIPS), 8317-8328, 2019 | 125 | 2019 |
| Combining neural networks with personalized pagerank for classification on graphs J Klicpera, A Bojchevski, S Günnemann International conference on learning representations, 2019 | 114 | 2019 |
| Robustness of graph neural networks at scale S Geisler, T Schmidt, H Şirin, D Zügner, A Bojchevski, S Günnemann Advances in Neural Information Processing Systems 34, 7637-7649, 2021 | 95 | 2021 |
| Bayesian robust attributed graph clustering: Joint learning of partial anomalies and group structure A Bojchevski, S Günnemann AAAI Conference on Artificial Intelligence, 2018 | 80 | 2018 |
| Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings A Bojchevski, Y Matkovic, S Günnemann International Conference on Knowledge Discovery and Data Mining (SIGKDD …, 2017 | 73 | 2017 |
| Efficient robustness certificates for discrete data: Sparsity-aware randomized smoothing for graphs, images and more A Bojchevski, J Gasteiger, S Günnemann International Conference on Machine Learning, 1003-1013, 2020 | 72 | 2020 |
| Dual-primal graph convolutional networks F Monti, O Shchur, A Bojchevski, O Litany, S Günnemann, MM Bronstein Graph Embedding and Mining, ECML-PKDD 2019 Workshop, 2018 | 69 | 2018 |
| Are defenses for graph neural networks robust? F Mujkanovic, S Geisler, S Günnemann, A Bojchevski Advances in Neural Information Processing Systems 35, 8954-8968, 2022 | 44 | 2022 |
| Generalization of neural combinatorial solvers through the lens of adversarial robustness S Geisler, J Sommer, J Schuchardt, A Bojchevski, S Günnemann arXiv preprint arXiv:2110.10942, 2021 | 31 | 2021 |
| LocText: relation extraction of protein localizations to assist database curation JM Cejuela, S Vinchurkar, T Goldberg, MS Prabhu Shankar, ... BMC bioinformatics 19, 1-11, 2018 | 30 | 2018 |
| Is pagerank all you need for scalable graph neural networks A Bojchevski, J Klicpera, B Perozzi, M Blais, A Kapoor, M Lukasik, ... ACM KDD, MLG Workshop, 2019 | 25 | 2019 |
| Or Litany, Stephan Günnemann, and Michael M Bronstein. Dual-primal graph convolutional networks F Monti, O Shchur, A Bojchevski arXiv preprint arXiv:1806.00770 3, 2018 | 23 | 2018 |
| 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 | 19 | 2020 |
| Completing the picture: Randomized smoothing suffers from the curse of dimensionality for a large family of distributions Y Wu, A Bojchevski, A Kuvshinov, S Günnemann International Conference on Artificial Intelligence and Statistics, 3763-3771, 2021 | 17 | 2021 |
Stephan GünnemannProfessor of Computer Science, Technical University of MunichDirección de correo verificada de in.tum.de
