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Stephan Günnemann
Stephan Günnemann
Professor of Computer Science, Technical University of Munich
Verified email at in.tum.de - Homepage
Title
Cited by
Cited by
Year
Predict then propagate: Graph neural networks meet personalized pagerank
J Gasteiger, A Bojchevski, S Günnemann
International Conference on Learning Representations (ICLR), 2019
1644*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
11322023
Pitfalls of graph neural network evaluation
O Shchur, M Mumme, A Bojchevski, S Günnemann
Relational Representation Learning Workshop, NeurIPS, 2018
10842018
Adversarial attacks on neural networks for graph data
D Zügner, A Akbarnejad, S Günnemann
ACM SIGKDD International Conference on Knowledge Discovery & Data Mining …, 2018
9632018
Directional message passing for molecular graphs
J Gasteiger, J Groß, S Günnemann
International Conference on Learning Representations (ICLR), 2020
7082020
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
A Bojchevski, S Günnemann
International Conference on Learning Representations (ICLR), 2018
6462018
Diffusion improves graph learning
J Gasteiger, S Weißenberger, S Günnemann
Neural Information Processing Systems (NeurIPS), 2019
5802019
Adversarial Attacks on Graph Neural Networks via Meta Learning
D Zügner, S Günnemann
International Conference on Learning Representations (ICLR), 2019
563*2019
Netgan: Generating graphs via random walks
A Bojchevski, O Shchur, D Zügner, S Günnemann
International Conference on Machine Learning (ICML), 2018
4062018
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
3612009
Gemnet: Universal directional graph neural networks for molecules
J Gasteiger, F Becker, S Günnemann
Advances in Neural Information Processing Systems 34, 6790-6802, 2021
329*2021
Adversarial attacks on node embeddings via graph poisoning
A Bojchevski, S Günnemann
International Conference on Machine Learning (ICML), 695-704, 2019
3172019
Failing loudly: An empirical study of methods for detecting dataset shift
S Rabanser, S Günnemann, ZC Lipton
Neural Information Processing Systems (NeurIPS), 2018
3152018
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
2582020
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
2462020
Introduction to tensor decompositions and their applications in machine learning
S Rabanser, O Shchur, S Günnemann
arXiv preprint arXiv:1711.10781, 2017
2462017
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
1712010
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
1602012
Certifiable robustness and robust training for graph convolutional networks
D Zügner, S Günnemann
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
1532019
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
1482010
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