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
Emmanuel MüllerProfessor of Computer Science, Technical University of DortmundVerified email at cs.tu-dortmund.de
Oleksandr ShchurApplied Scientist, AWS AIVerified email at amazon.com
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
Hardy KremerDeloitte Analytics Institute, GermanyVerified email at deloitte.de
Bertrand CharpentierTechnical University of MunichVerified email at in.tum.de
Thomas NeumannProfessor, Technische Universität MünchenVerified email at in.tum.de
Simon GeislerTechnical University of MunichVerified email at in.tum.de
Marin BilošPhD student, Technical University of MunichVerified email at in.tum.de
Armin MoinUniversity of California, Santa Barbara (UCSB)Verified email at ucsb.edu
Danai KoutraUniversity of Michigan, Ann ArborVerified email at umich.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
Mohit KumarUdaan, Flipkart, Accenture Technology Labs, Carnegie Mellon UniversityVerified email at udaan.com
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	1197*	2019
Pitfalls of graph neural network evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann Relational Representation Learning Workshop, NIPS 2018, 2018	763	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	753	2018
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2018	506	2018
Directional message passing for molecular graphs J Klicpera, J Groß, S Günnemann International Conference on Learning Representations (ICLR), 2020	490*	2020
Diffusion improves graph learning J Klicpera, S Weißenberger, S Günnemann Neural Information Processing Systems (NeurIPS), 2019	426*	2019
Adversarial attacks on graph neural networks via meta learning D Zügner, S Günnemann International Conference on Learning Representations (ICLR), 2019	418*	2019
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	355	2009
Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann ICML 2018, 2018	349	2018
Adversarial attacks on node embeddings via graph poisoning A Bojchevski, S Günnemann International Conference on Machine Learning, 695-704, 2019	254	2019
Failing loudly: An empirical study of methods for detecting dataset shift S Rabanser, S Günnemann, ZC Lipton Neural Information Processing Systems (NeurIPS), 2018	241	2018
Introduction to tensor decompositions and their applications in machine learning S Rabanser, O Shchur, S Günnemann arXiv preprint arXiv:1711.10781, 2017	218	2017
Gemnet: Universal directional graph neural networks for molecules J Gasteiger, F Becker, S Günnemann Advances in Neural Information Processing Systems 34, 6790-6802, 2021	175*	2021
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	171	2020
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	161	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	161	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	150	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	145	2010
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	135	2016
Com2: fast automatic discovery of temporal (‘comet’) communities M Araujo, S Papadimitriou, S Günnemann, C Faloutsos, P Basu, A Swami, ... Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia …, 2014	127	2014

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