Stephan Günnemann

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Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), GermanyVerified email at dbs.ifi.lmu.de
Aleksandar BojchevskiCISPA Helmholtz Center for Information SecurityVerified email at cispa.de
Daniel ZügnerMicrosoft ResearchVerified email at microsoft.com
Johannes Gasteiger, né KlicperaTechnical University of MunichVerified email at in.tum.de
Emmanuel MüllerProfessor of Computer Science, Technical University of DortmundVerified email at cs.tu-dortmund.de
Ines FärberData Management and Data ExplorationVerified email at cs.rwth-aachen.de
Christos FaloutsosCMUVerified email at cs.cmu.edu
Oleksandr ShchurPhD student, Technical University of MunichVerified email at in.tum.de
Ira AssentProfessor of Computer Science, Aarhus University, DenmarkVerified email at cs.au.dk
Hardy KremerDeloitte Analytics Institute, GermanyVerified email at deloitte.de
Thomas NeumannProfessor, Technische Universität MünchenVerified email at in.tum.de
Bertrand CharpentierTechnical University of MunichVerified email at in.tum.de
Marin BilošPhD student, Technical University of MunichVerified email at in.tum.de
Danai KoutraUniversity of Michigan, Ann ArborVerified email at umich.edu
Volker TrespLudwig Maximilian University of Munich / München (LMU) and Siemens AGVerified email at dbs.ifi.lmu.de
Simon GeislerTechnical University of MunichVerified email at in.tum.de
Mohit KumarUdaan, Flipkart, Accenture Technology Labs, Carnegie Mellon UniversityVerified email at udaan.com
Manuel ThenTechnische Universität MünchenVerified email at manuel-then.de
Armin MoinTechnical University of Munich (TUM)Verified email at in.tum.de
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	737*	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	567	2018
Pitfalls of graph neural network evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann Relational Representation Learning Workshop, NIPS 2018, 2018	489	2018
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2018	355*	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	340	2009
Adversarial attacks on graph neural networks via meta learning D Zügner, S Günnemann International Conference on Learning Representations (ICLR), 2019	290	2019
Directional message passing for molecular graphs J Klicpera, J Groß, S Günnemann International Conference on Learning Representations (ICLR), 2020	279	2020
Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann ICML 2018, 2018	272	2018
Diffusion improves graph learning J Klicpera, S Weißenberger, S Günnemann Neural Information Processing Systems (NeurIPS), 2019	249	2019
Adversarial attacks on node embeddings via graph poisoning A Bojchevski, S Günnemann International Conference on Machine Learning, 695-704, 2019	193	2019
Introduction to tensor decompositions and their applications in machine learning S Rabanser, O Shchur, S Günnemann arXiv preprint arXiv:1711.10781, 2017	186	2017
Failing loudly: An empirical study of methods for detecting dataset shift S Rabanser, S Günnemann, ZC Lipton Neural Information Processing Systems (NeurIPS), 2018	156	2018
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	155	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	146	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	120	2016
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	117	2014
Certifiable robustness and robust training for graph convolutional networks D Zügner, S Günnemann Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019	105	2019
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	100	2020
Relevant subspace clustering: Mining the most interesting non-redundant concepts in high dimensional data E Müller, I Assent, S Günnemann, R Krieger, T Seidl 2009 Ninth IEEE International Conference on Data Mining, 377-386, 2009	90	2009

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