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 BojchevskiTechnical University of MunichVerified email at in.tum.de
Daniel ZügnerTechnical 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
Johannes KlicperaTechnical University of MunichVerified email at in.tum.de
Christos FaloutsosCMUVerified email at cs.cmu.edu
Oleksandr ShchurPhD student, Technical University of MunichVerified email at in.tum.de
Hardy KremerDeloitte Analytics Institute, GermanyVerified email at deloitte.de
Thomas NeumannProfessor, Technische Universität MünchenVerified email at in.tum.de
Danai KoutraUniversity of Michigan, Ann ArborVerified email at umich.edu
Mohit KumarUdaan, Flipkart, Accenture Technology Labs, Carnegie Mellon UniversityVerified email at udaan.com
Manuel ThenTechnische Universität MünchenVerified email at manuel-then.de
Miguel AraujoSemasioVerified email at semasio.com
Neil ShahResearch Scientist at SnapVerified email at snap.com
Alex BeutelResearch Scientist, GoogleVerified email at google.com
Leman AkogluAssociate Professor of Information Systems, Heinz College, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Bryan HooiNational University of SingaporeVerified email at comp.nus.edu.sg
Harald LangTechnical University of MunichVerified email at in.tum.de
Evangelos E. PapalexakisAssociate Professor, University of California RiversideVerified email at cs.ucr.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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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	397	2018
Predict then propagate: Graph neural networks meet personalized pagerank J Klicpera, A Bojchevski, S Günnemann International Conference on Learning Representations, 2019, 2019	378*	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	337	2009
Pitfalls of graph neural network evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann Relational Representation Learning Workshop, NIPS 2018, 2018	253	2018
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2018	251*	2018
Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann ICML 2018, 2018	220	2018
Adversarial attacks on graph neural networks via meta learning D Zügner, S Günnemann International Conference on Learning Representations (ICLR), 2019	187	2019
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	140	2010
Directional message passing for molecular graphs J Klicpera, J Groß, S Günnemann International Conference on Learning Representations (ICLR), 2020	139	2020
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	138	2010
Introduction to tensor decompositions and their applications in machine learning S Rabanser, O Shchur, S Günnemann arXiv preprint arXiv:1711.10781, 2017	136	2017
Adversarial attacks on node embeddings via graph poisoning A Bojchevski, S Günnemann International Conference on Machine Learning, 695-704, 2019	133*	2019
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	132	2012
Diffusion improves graph learning J Klicpera, S Weißenberger, S Günnemann Neural Information Processing Systems (NeurIPS), 2019	112	2019
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	107	2014
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	102	2016
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	88	2009
Failing loudly: An empirical study of methods for detecting dataset shift S Rabanser, S Günnemann, ZC Lipton Neural Information Processing Systems (NeurIPS), 2018	81	2018
Certifiable robustness and robust training for graph convolutional networks D Zügner, S Günnemann Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019	78	2019
Discovering multiple clustering solutions: Grouping objects in different views of the data E Muller, S Gunnemann, I Farber, T Seidl 2012 IEEE 28th International Conference on Data Engineering, 1207-1210, 2012	70	2012

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