Stephan Günnemann - Google Scholar Citations

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	All	Since 2015
Citations	2419	1780
h-index	27	25
i10-index	52	41

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Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), GermanyVerified email at dbs.ifi.lmu.de
Emmanuel MüllerProfessor of Computer Science, Bonn-Aachen International Center for Information TechnologyVerified email at bit.uni-bonn.de
Ines FärberData Management and Data ExplorationVerified email at cs.rwth-aachen.de
Aleksandar BojchevskiTechnical University of MunichVerified email at in.tum.de
Ira AssentAssociate Professor of Computer Science, Aarhus University, DenmarkVerified email at cs.au.dk
Christos FaloutsosCMUVerified email at cs.cmu.edu
Hardy KremerDeloitte Analytics Institute, GermanyVerified email at deloitte.de
Daniel ZügnerTechnical University of MunichVerified email at in.tum.de
Oleksandr ShchurPhD student, Technical University of MunichVerified email at in.tum.de
Thomas NeumannProfessor, Technische Universität MünchenVerified email at in.tum.de
Danai KoutraUniversity of Michigan, Ann ArborVerified email at umich.edu
Miguel AraujoFeedzaiVerified email at feedzai.com
Mohit KumarFlipkart, Accenture Technology Labs, Carnegie Mellon UniversityVerified email at flipkart.com
Manuel ThenTechnische Universität MünchenVerified email at manuel-then.de
Johannes KlicperaTechnical University of MunichVerified email at in.tum.de
Neil ShahResearch Scientist, Snap Inc.Verified email at snap.com
Alex BeutelStaff Research 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
Evangelos E. PapalexakisAssistant 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 Data Mining Graphs Robustness


Title	Cited by	Year
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	291	2009
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	115	2010
Subspace clustering meets dense subgraph mining: A synthesis of two paradigms S Gunnemann, I Farber, B Boden, T Seidl 2010 IEEE International Conference on Data Mining, 845-850, 2010	114	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	99	2012
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2018	92*	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	87	2018
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	80	2009
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	79	2014
Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann ICML 2018, 2018	72	2018
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	66	2012
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	64	2016
DB-CSC: a density-based approach for subspace clustering in graphs with feature vectors S Günnemann, B Boden, T Seidl Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011	59	2011
Detecting anomalies in dynamic rating data: A robust probabilistic model for rating evolution S Günnemann, N Günnemann, C Faloutsos Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014	49	2014
External evaluation measures for subspace clustering S Günnemann, I Färber, E Müller, I Assent, T Seidl Proceedings of the 20th ACM international conference on Information and …, 2011	49	2011
Spectral subspace clustering for graphs with feature vectors S Günnemann, I Färber, S Raubach, T Seidl 2013 IEEE 13th International Conference on Data Mining, 231-240, 2013	46	2013
DensEst: Density estimation for data mining in high dimensional spaces E Müller, I Assent, R Krieger, S Günnemann, T Seidl Proceedings of the 2009 SIAM international conference on data mining, 175-186, 2009	45	2009
Detection of orthogonal concepts in subspaces of high dimensional data S Günnemann, E Müller, I Färber, T Seidl Proceedings of the 18th ACM conference on Information and knowledge …, 2009	41	2009
Efficient mining of combined subspace and subgraph clusters in graphs with feature vectors S Günnemann, B Boden, I Färber, T Seidl Pacific-Asia Conference on Knowledge Discovery and Data Mining, 261-275, 2013	39	2013
Linearized and single-pass belief propagation W Gatterbauer, S Günnemann, D Koutra, C Faloutsos arXiv preprint arXiv:1406.7288, 2014	38	2014
Pitfalls of graph neural network evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann arXiv preprint arXiv:1811.05868, 2018	37	2018

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