Seuraa
Stephan Günnemann
Stephan Günnemann
Professor of Computer Science, Technical University of Munich
Vahvistettu sähköpostiosoite verkkotunnuksessa in.tum.de - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
Predict then propagate: Graph neural networks meet personalized pagerank
J Klicpera, A Bojchevski, S Günnemann
International Conference on Learning Representations, 2019, 2019
787*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
6042018
Pitfalls of graph neural network evaluation
O Shchur, M Mumme, A Bojchevski, S Günnemann
Relational Representation Learning Workshop, NIPS 2018, 2018
5202018
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
A Bojchevski, S Günnemann
International Conference on Learning Representations (ICLR), 2018
375*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
Directional message passing for molecular graphs
J Klicpera, J Groß, S Günnemann
International Conference on Learning Representations (ICLR), 2020
3072020
Adversarial attacks on graph neural networks via meta learning
D Zügner, S Günnemann
International Conference on Learning Representations (ICLR), 2019
3072019
Netgan: Generating graphs via random walks
A Bojchevski, O Shchur, D Zügner, S Günnemann
ICML 2018, 2018
2842018
Diffusion improves graph learning
J Klicpera, S Weißenberger, S Günnemann
Neural Information Processing Systems (NeurIPS), 2019
2712019
Adversarial attacks on node embeddings via graph poisoning
A Bojchevski, S Günnemann
International Conference on Machine Learning, 695-704, 2019
1992019
Introduction to tensor decompositions and their applications in machine learning
S Rabanser, O Shchur, S Günnemann
arXiv preprint arXiv:1711.10781, 2017
1952017
Failing loudly: An empirical study of methods for detecting dataset shift
S Rabanser, S Günnemann, ZC Lipton
Neural Information Processing Systems (NeurIPS), 2018
1672018
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
1222016
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
1072020
Certifiable robustness and robust training for graph convolutional networks
D Zügner, S Günnemann
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
1072019
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
1022020
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Artikkelit 1–20