Stephan Günnemann

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Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), MCML, GermanyVerified email at dbs.ifi.lmu.de
Aleksandar BojchevskiUniversity of CologneVerified email at uni-koeln.de
Johannes Gasteiger, né KlicperaGoogle ResearchVerified email at google.com
Daniel ZügnerMicrosoft ResearchVerified email at microsoft.com
Oleksandr ShchurApplied Scientist, AWS AIVerified email at amazon.com
Emmanuel MüllerProfessor of Computer Science, Technical University of DortmundVerified email at cs.tu-dortmund.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
Bertrand CharpentierTechnical University of MunichVerified email at in.tum.de
Simon GeislerTechnical University of MunichVerified email at in.tum.de
Hardy KremerDeloitte Analytics Institute, GermanyVerified email at deloitte.de
Marin BilošPhD student, Technical University of MunichVerified email at in.tum.de
Thomas NeumannProfessor, Technische Universität MünchenVerified email at in.tum.de
Karsten RoscherResearcher, Fraunhofer IKSVerified email at iks.fraunhofer.de
Armin MoinProfessor of Computer Science at the University of Colorado Colorado Springs (UCCS)Verified email at uccs.edu
Neil ShahResearch Scientist at SnapVerified email at snap.com
Volker TrespLudwig Maximilian University of Munich / München (LMU)Verified email at dbs.ifi.lmu.de
Oliver BorchertMSc Data Engineering and Analytics, Technical University of MunichVerified email at tum.de
Stephan RabanserUniversity of Toronto & Vector Institute for Artificial IntelligenceVerified email at cs.toronto.edu

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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Predict then propagate: Graph neural networks meet personalized pagerank J Klicpera, A Bojchevski, S Günnemann International Conference on Learning Representations, 2019, 2019	1499*	2019
Pitfalls of graph neural network evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann Relational Representation Learning Workshop, NIPS 2018, 2018	978	2018
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	894	2018
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	712	2023
Directional message passing for molecular graphs J Klicpera, J Groß, S Günnemann International Conference on Learning Representations (ICLR), 2020	627*	2020
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2018	602	2018
Diffusion improves graph learning J Klicpera, S Weißenberger, S Günnemann Neural Information Processing Systems (NeurIPS), 2019	537*	2019
Adversarial attacks on graph neural networks via meta learning D Zügner, S Günnemann International Conference on Learning Representations (ICLR), 2019	507*	2019
Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann ICML 2018, 2018	389	2018
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	361	2009
Adversarial attacks on node embeddings via graph poisoning A Bojchevski, S Günnemann International Conference on Machine Learning, 695-704, 2019	298	2019
Failing loudly: An empirical study of methods for detecting dataset shift S Rabanser, S Günnemann, ZC Lipton Neural Information Processing Systems (NeurIPS), 2018	294	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	277*	2021
Introduction to tensor decompositions and their applications in machine learning S Rabanser, O Shchur, S Günnemann arXiv preprint arXiv:1711.10781, 2017	233	2017
Fast and uncertainty-aware directional message passing for non-equilibrium molecules J Gasteiger, S Giri, JT Margraf, S Günnemann arXiv preprint arXiv:2011.14115, 2020	224	2020
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	222	2020
On using class-labels in evaluation of clusterings I Färber, S Günnemann, HP Kriegel, P Kröger, E Müller, E Schubert, ... MultiClust: 1st international workshop on discovering, summarizing and using …, 2010	171	2010
Mining coherent subgraphs in multi-layer graphs with edge labels B Boden, S Günnemann, H Hoffmann, T Seidl Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012	157	2012
Subspace clustering meets dense subgraph mining: A synthesis of two paradigms S Günnemann, I Färber, B Boden, T Seidl 2010 IEEE international conference on data mining, 845-850, 2010	147	2010
Certifiable robustness and robust training for graph convolutional networks D Zügner, S Günnemann Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019	144	2019

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