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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 Klicpera, A Bojchevski, S Günnemann
International Conference on Learning Representations, 2019, 2019
796*2019
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
6082018
Pitfalls of graph neural network evaluation
O Shchur, M Mumme, A Bojchevski, S Günnemann
Relational Representation Learning Workshop, NIPS 2018, 2018
5262018
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
A Bojchevski, S Günnemann
International Conference on Learning Representations (ICLR), 2018
379*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
3422009
Adversarial attacks on graph neural networks via meta learning
D Zügner, S Günnemann
International Conference on Learning Representations (ICLR), 2019
3102019
Directional message passing for molecular graphs
J Klicpera, J Groß, S Günnemann
International Conference on Learning Representations (ICLR), 2020
3082020
Netgan: Generating graphs via random walks
A Bojchevski, O Shchur, D Zügner, S Günnemann
ICML 2018, 2018
2852018
Diffusion improves graph learning
J Klicpera, S Weißenberger, S Günnemann
Neural Information Processing Systems (NeurIPS), 2019
2722019
Adversarial attacks on node embeddings via graph poisoning
A Bojchevski, S Günnemann
International Conference on Machine Learning, 695-704, 2019
2022019
Introduction to tensor decompositions and their applications in machine learning
S Rabanser, O Shchur, S Günnemann
arXiv preprint arXiv:1711.10781, 2017
1962017
Failing loudly: An empirical study of methods for detecting dataset shift
S Rabanser, S Günnemann, ZC Lipton
Neural Information Processing Systems (NeurIPS), 2018
1682018
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
1562010
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
1462012
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
1452010
Birdnest: Bayesian inference for ratings-fraud detection
B Hooi, N Shah, A Beutel, S Günnemann, L Akoglu, M Kumar, D Makhija, ...
Proceedings of the 2016 SIAM International Conference on Data Mining, 495-503, 2016
1232016
Com2: fast automatic discovery of temporal (‘comet’) communities
M Araujo, S Papadimitriou, S Günnemann, C Faloutsos, P Basu, A Swami, ...
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 271-283, 2014
1172014
Scaling graph neural networks with approximate pagerank
A Bojchevski, J Klicpera, B Perozzi, A Kapoor, M Blais, B Rózemberczki, ...
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
1082020
Certifiable robustness and robust training for graph convolutional networks
D Zügner, S Günnemann
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
1082019
Fast and uncertainty-aware directional message passing for non-equilibrium molecules
J Klicpera, S Giri, JT Margraf, S Günnemann
arXiv preprint arXiv:2011.14115, 2020
1032020
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Articles 1–20