Stephan Günnemann

Citirano

	Sve	Od 2017.
Citati	7972	6772
H-indeks	42	35
i10-indeks	98	84

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Suautori

Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), GermanyPotvrđena adresa e-pošte na dbs.ifi.lmu.de
Aleksandar BojchevskiCISPA Helmholtz Center for Information SecurityPotvrđena adresa e-pošte na cispa.de
Daniel ZügnerMicrosoft ResearchPotvrđena adresa e-pošte na microsoft.com
Johannes Gasteiger, né KlicperaTechnical University of MunichPotvrđena adresa e-pošte na in.tum.de
Emmanuel MüllerProfessor of Computer Science, Technical University of DortmundPotvrđena adresa e-pošte na cs.tu-dortmund.de
Ines FärberData Management and Data ExplorationPotvrđena adresa e-pošte na cs.rwth-aachen.de
Christos FaloutsosCMUPotvrđena adresa e-pošte na cs.cmu.edu
Oleksandr ShchurPhD student, Technical University of MunichPotvrđena adresa e-pošte na in.tum.de
Ira AssentProfessor of Computer Science, Aarhus University, DenmarkPotvrđena adresa e-pošte na cs.au.dk
Hardy KremerDeloitte Analytics Institute, GermanyPotvrđena adresa e-pošte na deloitte.de
Thomas NeumannProfessor, Technische Universität MünchenPotvrđena adresa e-pošte na in.tum.de
Bertrand CharpentierTechnical University of MunichPotvrđena adresa e-pošte na in.tum.de
Marin BilošPhD student, Technical University of MunichPotvrđena adresa e-pošte na in.tum.de
Danai KoutraUniversity of Michigan, Ann ArborPotvrđena adresa e-pošte na umich.edu
Simon GeislerTechnical University of MunichPotvrđena adresa e-pošte na in.tum.de
Volker TrespLudwig Maximilian University of Munich / München (LMU) and Siemens AGPotvrđena adresa e-pošte na dbs.ifi.lmu.de
Mohit KumarUdaan, Flipkart, Accenture Technology Labs, Carnegie Mellon UniversityPotvrđena adresa e-pošte na udaan.com
Armin MoinTechnical University of Munich (TUM)Potvrđena adresa e-pošte na in.tum.de
Manuel ThenTechnische Universität MünchenPotvrđena adresa e-pošte na manuel-then.de
Stephan RabanserUniversity of Toronto & Vector Institute for Artificial IntelligencePotvrđena adresa e-pošte na cs.toronto.edu

Prati

Stephan Günnemann

Professor of Computer Science, Technical University of Munich

Potvrđena adresa e-pošte na in.tum.de - Početna stranica

Machine Learning Graphs Graph Neural Networks Robustness


Naslov Poredaj po navodima Poredaj po godini Poredaj po naslovu	Citirano Citirano	Godina
Predict then propagate: Graph neural networks meet personalized pagerank J Klicpera, A Bojchevski, S Günnemann International Conference on Learning Representations, 2019, 2019	751*	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	577	2018
Pitfalls of graph neural network evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann Relational Representation Learning Workshop, NIPS 2018, 2018	496	2018
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2018	358*	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	294	2019
Directional message passing for molecular graphs J Klicpera, J Groß, S Günnemann International Conference on Learning Representations (ICLR), 2020	287	2020
Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann ICML 2018, 2018	275	2018
Diffusion improves graph learning J Klicpera, S Weißenberger, S Günnemann Neural Information Processing Systems (NeurIPS), 2019	255	2019
Adversarial attacks on node embeddings via graph poisoning A Bojchevski, S Günnemann International Conference on Machine Learning, 695-704, 2019	194	2019
Introduction to tensor decompositions and their applications in machine learning S Rabanser, O Shchur, S Günnemann arXiv preprint arXiv:1711.10781, 2017	188	2017
Failing loudly: An empirical study of methods for detecting dataset shift S Rabanser, S Günnemann, ZC Lipton Neural Information Processing Systems (NeurIPS), 2018	157	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	106	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	101	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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