Stephan Günnemann
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Stephan Günnemann is a person.
References
2020
- (Bojchevski et al., 2020) ⇒ Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann. (2020). “Scaling Graph Neural Networks with Approximate PageRank.” In: arXiv preprint arXiv:2007.01570.
2018
- (Zügner et al., 2018) ⇒ Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann. (2018). “Adversarial Attacks on Neural Networks for Graph Data.” In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2847-2856.
- (Bojchevski & Günnemann, 2018) ⇒ Aleksandar Bojchevski, and Stephan Günnemann. (2018). “Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking.” In: International Conference on Learning Representations (ICLR-2018).
2014
- (Günnemann et al., 2014) ⇒ Stephan Günnemann, Ines Färber, Matthias Rüdiger, and Thomas Seidl. (2014). “SMVC: Semi-supervised Multi-view Clustering in Subspace Projections.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623734
- (Günnemann et al., 2014) ⇒ Stephan Günnemann, Nikou Günnemann, and Christos Faloutsos. (2014). “Detecting Anomalies in Dynamic Rating Data: A Robust Probabilistic Model for Rating Evolution.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623721
2012
- (Günnemann et al., 2012) ⇒ Stephan Günnemann, Ines Färber, Kittipat Virochsiri, and Thomas Seidl. (2012). “Subspace Correlation Clustering: Finding Locally Correlated Dimensions in Subspace Projections of the Data.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339588
- (Günnemann et al., 2012) ⇒ Stephan Günnemann, Ines Färber, and Thomas Seidl. (2012). “Multi-view Clustering Using Mixture Models in Subspace Projections.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339553
- (Boden et al., 2012) ⇒ Brigitte Boden, Stephan Günnemann, Holger Hoffmann, and Thomas Seidl. (2012). “Mining Coherent Subgraphs in Multi-layer Graphs with Edge Labels.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339726
2009
- (Müller et al., 2009) ⇒ Emmanuel Müller, Stephan Günnemann, Ira Assent, and Thomas Seidl. (2009). “Evaluating Clustering in Subspace Projections of High Dimensional Data.” Proceedings of the VLDB Endowment, 2(1).