Stephan Günnemann

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Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), MCML, GermanyDirección de correo verificada de dbs.ifi.lmu.de
Aleksandar BojchevskiUniversity of CologneDirección de correo verificada de uni-koeln.de
Johannes Gasteiger, né KlicperaGoogle ResearchDirección de correo verificada de google.com
Daniel ZügnerMicrosoft ResearchDirección de correo verificada de microsoft.com
Oleksandr ShchurApplied Scientist, AWS AIDirección de correo verificada de amazon.com
Emmanuel MüllerProfessor of Computer Science, Technical University of DortmundDirección de correo verificada de cs.tu-dortmund.de
Christos FaloutsosCMUDirección de correo verificada de cs.cmu.edu
Ines FärberData Management and Data ExplorationDirección de correo verificada de cs.rwth-aachen.de
Bertrand CharpentierTechnical University of MunichDirección de correo verificada de in.tum.de
Ira AssentProfessor of Computer Science, Aarhus University, Denmark; IAS-8, Forschungszentrum JülichDirección de correo verificada de cs.au.dk
Simon GeislerTechnical University of MunichDirección de correo verificada de in.tum.de
Hardy KremerDeloitte Analytics Institute, GermanyDirección de correo verificada de deloitte.de
Marin BilošPhD student, Technical University of MunichDirección de correo verificada de in.tum.de
Thomas NeumannProfessor, Technische Universität MünchenDirección de correo verificada de in.tum.de
Karsten RoscherResearcher, Fraunhofer IKSDirección de correo verificada de iks.fraunhofer.de
Armin MoinProfessor of Computer Science at the University of Colorado Colorado Springs (UCCS)Dirección de correo verificada de uccs.edu
Gitta KutyniokBavarian AI Chair for Mathematical Foundations of Artificial Intelligence, LMU MunichDirección de correo verificada de math.lmu.de
Jan SchuchardtTechnical University of MunichDirección de correo verificada de in.tum.de
Nicholas GaoTechnical University of MunichDirección de correo verificada de tum.de
Volker TrespLudwig Maximilian University of Munich / München (LMU)Dirección de correo verificada de dbs.ifi.lmu.de

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Stephan Günnemann

Professor of Computer Science, Technical University of Munich

Dirección de correo verificada de in.tum.de - Página principal

Machine Learning Graphs Graph Neural Networks Robustness


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Predict then propagate: Graph neural networks meet personalized pagerank J Gasteiger, A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2019	1734*	2019
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	1562	2023
Pitfalls of graph neural network evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann Relational Representation Learning Workshop, NeurIPS, 2018	1149	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	996	2018
Directional message passing for molecular graphs J Gasteiger, J Groß, S Günnemann International Conference on Learning Representations (ICLR), 2020	753	2020
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2018	676	2018
Diffusion improves graph learning J Gasteiger, S Weißenberger, S Günnemann Neural Information Processing Systems (NeurIPS), 2019	624	2019
Adversarial Attacks on Graph Neural Networks via Meta Learning D Zügner, S Günnemann International Conference on Learning Representations (ICLR), 2019	591*	2019
Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann International Conference on Machine Learning (ICML), 2018	415	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	363	2009
Gemnet: Universal directional graph neural networks for molecules J Gasteiger, F Becker, S Günnemann Advances in Neural Information Processing Systems 34, 6790-6802, 2021	354*	2021
Failing loudly: An empirical study of methods for detecting dataset shift S Rabanser, S Günnemann, ZC Lipton Neural Information Processing Systems (NeurIPS), 2018	328	2018
Adversarial attacks on node embeddings via graph poisoning A Bojchevski, S Günnemann International Conference on Machine Learning (ICML), 695-704, 2019	327	2019
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	286	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	261	2020
Introduction to tensor decompositions and their applications in machine learning S Rabanser, O Shchur, S Günnemann arXiv preprint arXiv:1711.10781, 2017	259	2017
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	170	2010
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	163	2022
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	158	2012
Certifiable robustness and robust training for graph convolutional networks D Zügner, S Günnemann Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019	155	2019

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