His scientific interests lie mostly in Artificial intelligence, Data mining, Cluster analysis, Graph and Theoretical computer science. His work deals with themes such as Machine learning and Computation, which intersect with Artificial intelligence. The concepts of his Data mining study are interwoven with issues in Correlation clustering and Fuzzy clustering.
Stephan Günnemann is studying Clustering high-dimensional data, which is a component of Cluster analysis. His study looks at the relationship between Graph and topics such as Algorithm, which overlap with Attack model, Spectral clustering and Spectral method. His Theoretical computer science research incorporates elements of Adversarial system, Embedding, Representation, Ranking and Random walk.
Stephan Günnemann mainly investigates Artificial intelligence, Data mining, Theoretical computer science, Graph and Cluster analysis. He has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition. His Data mining research is multidisciplinary, incorporating perspectives in Scalability, Multivariate statistics and Tracing.
His Theoretical computer science research is multidisciplinary, incorporating elements of Adversarial system, Graph, Structure, Computation and Random walk. Stephan Günnemann has included themes like Artificial neural network, Computational complexity theory, Algorithm and PageRank in his Graph study. Stephan Günnemann combines subjects such as Subspace topology and Linear subspace with his study of Cluster analysis.
Stephan Günnemann mostly deals with Artificial intelligence, Algorithm, Robustness, Theoretical computer science and Graph. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Code and Natural language processing. His Robustness research includes elements of Smoothing, Feature and Semi-supervised learning.
The study incorporates disciplines such as Relational database, Graph and Scaling in addition to Theoretical computer science. His research in the fields of Graph neural networks overlaps with other disciplines such as Scene graph. His work on Graph neural networks is being expanded to include thematically relevant topics such as Data mining.
Stephan Günnemann mainly focuses on Graph, Theoretical computer science, Algorithm, Message passing and Artificial neural network. The Graph study combines topics in areas such as Graph and Artificial intelligence. His research integrates issues of Basis and Natural language processing in his study of Artificial intelligence.
His studies deal with areas such as Relational database, Graph neural networks and Scaling as well as Theoretical computer science. His biological study spans a wide range of topics, including Equivariant map and Topology. His Artificial neural network study incorporates themes from Calibration, Uncertainty quantification, Inference, Posterior probability and Dropout.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Evaluating Clustering in Subspace Projections of High Dimensional Data
Emmanuel Müller;Stephan Günnemann;Ira Assent;Thomas Seidl.
The Vldb Journal (2009)
Pitfalls of Graph Neural Network Evaluation.
Oleksandr Shchur;Maximilian Mumme;Aleksandar Bojchevski;Stephan Günnemann.
arXiv: Learning (2018)
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Aleksandar Bojchevski;Stephan Günnemann.
international conference on learning representations (2018)
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Johannes Klicpera;Aleksandar Bojchevski;Stephan Günnemann.
international conference on learning representations (2018)
Adversarial Attacks on Graph Neural Networks via Meta Learning
Daniel Zügner;Stephan Günnemann.
international conference on learning representations (2019)
Adversarial Attacks on Neural Networks for Graph Data.
Daniel Zügner;Amir Akbarnejad;Stephan Günnemann.
international joint conference on artificial intelligence (2019)
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms
Stephan Gunnemann;Ines Farber;Brigitte Boden;Thomas Seidl.
international conference on data mining (2010)
On Using Class-Labels in Evaluation of Clusterings
Ines Färber;Stephan Günnemann;Hans-Peter Kriegel;Peer Kröger.
(2010)
Mining coherent subgraphs in multi-layer graphs with edge labels
Brigitte Boden;Stephan Günnemann;Holger Hoffmann;Thomas Seidl.
knowledge discovery and data mining (2012)
Directional Message Passing for Molecular Graphs
Johannes Klicpera;Janek Groß;Stephan Günnemann.
international conference on learning representations (2020)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
Ludwig-Maximilians-Universität München
Carnegie Mellon University
Technical University of Munich
Technical University of Munich
Ludwig-Maximilians-Universität München
Carnegie Mellon University
Volkswagen Group (United States)
United States Army Research Laboratory
Rutgers, The State University of New Jersey
Carnegie Mellon University
Singapore University of Technology and Design
University of Illinois at Urbana-Champaign
Amazon (United States)
Nanjing University
University of Washington
University of Illinois at Urbana-Champaign
Seoul National University
University of Groningen
University of California, Berkeley
Lund University
Commonwealth Scientific and Industrial Research Organisation
University of Nottingham
University of Florida
University of Geneva
Beth Israel Deaconess Medical Center
University of Pittsburgh