Stephan Günnemann

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Citations	21768	19834
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Aleksandar BojchevskiUniversity of CologneVerified email at uni-koeln.de
Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), MCML, GermanyVerified email at dbs.ifi.lmu.de
Johannes Gasteiger, né KlicperaAnthropicVerified email at anthropic.com
Daniel ZügnerMicrosoft ResearchVerified email at microsoft.com
Oleksandr ShchurApplied Scientist, AWS AIVerified email at amazon.com
Simon GeislerTechnical University of MunichVerified email at in.tum.de
Emmanuel MüllerProfessor of Computer Science, Technical University of DortmundVerified email at cs.tu-dortmund.de
Bertrand CharpentierTechnical University of MunichVerified email at in.tum.de
Christos FaloutsosCMUVerified email at cs.cmu.edu
Ines FärberData Management and Data ExplorationVerified email at cs.rwth-aachen.de
Ira AssentProfessor of Computer Science, Aarhus University, Denmark; IAS-8, Forschungszentrum JülichVerified email at cs.au.dk
Hardy KremerDeloitte Analytics Institute, GermanyVerified email at deloitte.de
Marin BilošPhD student, Technical University of MunichVerified email at in.tum.de
Karsten RoscherResearcher, Fraunhofer IKSVerified email at iks.fraunhofer.de
Thomas NeumannProfessor, Technische Universität MünchenVerified email at in.tum.de
Nicholas GaoTechnical University of MunichVerified email at tum.de
Gitta KutyniokBavarian AI Chair for Mathematical Foundations of Artificial Intelligence, LMU MunichVerified email at math.lmu.de
Armin MoinAssistant Professor of Computer Science at the University of Colorado Colorado Springs (UCCS)Verified email at uccs.edu
Jan SchuchardtTechnical University of MunichVerified email at in.tum.de
Leon HetzelPhD Student @Helmholtz Munich & TUMVerified email at in.tum.de

Stephan Günnemann

Professor of Computer Science, Technical University of Munich

Verified email at in.tum.de - Homepage

Machine Learning Graphs Graph Neural Networks Robustness


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ChatGPT for good? On opportunities and challenges of large language models for education E Kasneci, K Seßler, S Küchemann, M Bannert, D Dementieva, F Fischer, ... Learning and individual differences 103, 102274, 2023	3248	2023
Predict then propagate: Graph neural networks meet personalized pagerank J Gasteiger, A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2019	2100	2019
Pitfalls of graph neural network evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann Relational Representation Learning Workshop, NeurIPS, 2018	1421	2018
Adversarial attacks on neural networks for graph data D Zügner, A Akbarnejad, S Günnemann ACM SIGKDD International Conference on Knowledge Discovery & Data Mining …, 2018	1129	2018
Directional message passing for molecular graphs J Gasteiger, J Groß, S Günnemann International Conference on Learning Representations (ICLR), 2020	945	2020
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2018	781	2018
Diffusion improves graph learning J Gasteiger, S Weißenberger, S Günnemann Neural Information Processing Systems (NeurIPS), 2019	772	2019
Adversarial Attacks on Graph Neural Networks via Meta Learning D Zügner, S Günnemann International Conference on Learning Representations (ICLR), 2019	687*	2019
Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann International Conference on Machine Learning (ICML), 2018	466	2018
Gemnet: Universal directional graph neural networks for molecules J Gasteiger, F Becker, S Günnemann Advances in Neural Information Processing Systems 34, 6790-6802, 2021	465*	2021
Failing loudly: An empirical study of methods for detecting dataset shift S Rabanser, S Günnemann, ZC Lipton Neural Information Processing Systems (NeurIPS), 2018	398	2018
Fast and uncertainty-aware directional message passing for non-equilibrium molecules J Gasteiger, S Giri, JT Margraf, S Günnemann Machine Learning for Molecules Workshop, NeurIPS, 2020	375	2020
Evaluating clustering in subspace projections of high dimensional data E Müller, S Günnemann, I Assent, T Seidl Proceedings of the VLDB Endowment 2 (1), 1270-1281, 2009	366	2009
Adversarial attacks on node embeddings via graph poisoning A Bojchevski, S Günnemann International Conference on Machine Learning (ICML), 695-704, 2019	360	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	306	2020
Introduction to tensor decompositions and their applications in machine learning S Rabanser, O Shchur, S Günnemann arXiv preprint arXiv:1711.10781, 2017	287	2017
3d infomax improves gnns for molecular property prediction H Stärk, D Beaini, G Corso, P Tossou, C Dallago, S Günnemann, P Liò International Conference on Machine Learning, 20479-20502, 2022	225	2022
Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts B Charpentier, D Zügner, S Günnemann Neural Information Processing Systems (NeurIPS), 2020	196	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	179	2019
Intensity-free learning of temporal point processes O Shchur, M Biloš, S Günnemann International Conference on Learning Representations (ICLR), 2019	177	2019

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