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ORCIDStephan Günnemann
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- affiliation: Technical University of Munich, Germany
- affiliation (former): Carnegie Mellon University, Pittsburgh, USA
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2020 – today
- 2022
[j18]Artur Mrowca, Florian Gyrock, Stephan Günnemann:
Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation. Data Min. Knowl. Discov. 36(1): 240-294 (2022)- 2021
- [j17]Martin Atzmueller
, Stephan Günnemann, Albrecht Zimmermann:
Mining communities and their descriptions on attributed graphs: a survey. Data Min. Knowl. Discov. 35(3): 661-687 (2021) - [j16]Anna-Kathrin Kopetzki, Stephan Günnemann:
Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training. Mach. Learn. 110(6): 1175-1197 (2021) - [c112]Yihan Wu, Aleksandar Bojchevski, Aleksei Kuvshinov, Stephan Günnemann:
Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions. AISTATS 2021: 3763-3771 - [c111]Jan Schuchardt, Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann:
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. ICLR 2021 - [c110]Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann:
Language-Agnostic Representation Learning of Source Code from Structure and Context. ICLR 2021 - [c109]Marin Bilos, Stephan Günnemann:
Scalable Normalizing Flows for Permutation Invariant Densities. ICML 2021: 957-967 - [c108]Johannes Klicpera, Marten Lienen, Stephan Günnemann:
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. ICML 2021: 5616-5627 - [c107]Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann:
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? ICML 2021: 5707-5718 - [c106]Tom Haider, Felippe Schmoeller Roza, Dirk Eilers, Karsten Roscher, Stephan Günnemann:
Domain Shifts in Reinforcement Learning: Identifying Disturbances in Environments. AISafety@IJCAI 2021 - [c105]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann:
Neural Temporal Point Processes: A Review. IJCAI 2021: 4585-4593 - [c104]Rajat Koner, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, Stephan Günnemann:
Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering. ISWC 2021: 111-127 - [c103]Maximilian E. Schüle, Harald Lang, Maximilian Springer, Alfons Kemper, Thomas Neumann, Stephan Günnemann:
In-Database Machine Learning with SQL on GPUs. SSDBM 2021: 25-36 - [i66]Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann:
Language-Agnostic Representation Learning of Source Code from Structure and Context. CoRR abs/2103.11318 (2021) - [i65]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann:
Neural Temporal Point Processes: A Review. CoRR abs/2104.03528 (2021) - [i64]Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann:
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. CoRR abs/2105.04471 (2021) - [i63]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann:
Detecting Anomalous Event Sequences with Temporal Point Processes. CoRR abs/2106.04465 (2021) - [i62]Johannes Klicpera, Florian Becker, Stephan Günnemann:
GemNet: Universal Directional Graph Neural Networks for Molecules. CoRR abs/2106.08903 (2021) - [i61]Armin Moin, Atta Badii, Stephan Günnemann:
A Model-Driven Engineering Approach to Machine Learning and Software Modeling. CoRR abs/2107.02689 (2021) - [i60]Armin Moin, Atta Badii, Stephan Günnemann:
Enabling Un-/Semi-Supervised Machine Learning for MDSE of the Real-World CPS/IoT Applications. CoRR abs/2107.02690 (2021) - [i59]Armin Moin, Andrei Mituca, Atta Badii, Stephan Günnemann:
ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services. CoRR abs/2107.02692 (2021) - [i58]Rajat Koner, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, Stephan Günnemann:
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering. CoRR abs/2107.06325 (2021) - [i57]Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann:
MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence. CoRR abs/2107.06708 (2021) - [i56]Johannes Klicpera, Marten Lienen, Stephan Günnemann:
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. CoRR abs/2107.06876 (2021) - [i55]Sven Elflein, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
On Out-of-distribution Detection with Energy-based Models. CoRR abs/2107.08785 (2021) - [i54]Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher, Stephan Günnemann, Volker Tresp:
OODformer: Out-Of-Distribution Detection Transformer. CoRR abs/2107.08976 (2021) - [i53]Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Mihail I. Todorov, Anjany Sekuboyina, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, Bjoern H. Menze:
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph). CoRR abs/2108.13233 (2021) - [i52]Sebastian Bischoff, Stephan Günnemann, Martin Jaggi, Sebastian U. Stich:
On Second-order Optimization Methods for Federated Learning. CoRR abs/2109.02388 (2021) - [i51]Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan Günnemann, Jan Gasthaus:
A Study of Joint Graph Inference and Forecasting. CoRR abs/2109.04979 (2021) - [i50]Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Liò:
3D Infomax improves GNNs for Molecular Property Prediction. CoRR abs/2110.04126 (2021) - [i49]Nicholas Gao, Stephan Günnemann:
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions. CoRR abs/2110.05064 (2021) - [i48]Peter Súkeník, Aleksei Kuvshinov, Stephan Günnemann:
Intriguing Properties of Input-dependent Randomized Smoothing. CoRR abs/2110.05365 (2021) - [i47]Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann:
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. CoRR abs/2110.10942 (2021) - [i46]Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann:
Neural Flows: Efficient Alternative to Neural ODEs. CoRR abs/2110.13040 (2021) - [i45]Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann:
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. CoRR abs/2110.14012 (2021) - [i44]Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann:
Robustness of Graph Neural Networks at Scale. CoRR abs/2110.14038 (2021) - [i43]Johannes Klicpera, Chandan Yeshwanth, Stephan Günnemann:
Directional Message Passing on Molecular Graphs via Synthetic Coordinates. CoRR abs/2111.04718 (2021) - 2020
- [j15]Daniel Zügner, Oliver Borchert, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns. ACM Trans. Knowl. Discov. Data 14(5): 57:1-57:31 (2020) - [c102]Zhen Han, Yunpu Ma, Yuyi Wang, Stephan Günnemann, Volker Tresp:
Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs. AKBC 2020 - [c101]Eugenio Angriman, Alexander van der Grinten, Aleksandar Bojchevski, Daniel Zügner, Stephan Günnemann, Henning Meyerhenke:
Group Centrality Maximization for Large-scale Graphs. ALENEX 2020: 56-69 - [c100]Felippe Schmoeller Roza, Maximilian Henne, Karsten Roscher, Stephan Günnemann:
Assessing Box Merging Strategies and Uncertainty Estimation Methods in Multimodel Object Detection. ECCV Workshops (6) 2020: 3-10 - [c99]Johannes Klicpera, Janek Groß, Stephan Günnemann:
Directional Message Passing for Molecular Graphs. ICLR 2020 - [c98]Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt, Stephan Günnemann:
Continual Learning with Bayesian Neural Networks for Non-Stationary Data. ICLR 2020 - [c97]Oleksandr Shchur, Marin Bilos, Stephan Günnemann:
Intensity-Free Learning of Temporal Point Processes. ICLR 2020 - [c96]Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann:
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. ICML 2020: 1003-1013 - [c95]Daniel Zügner, Stephan Günnemann:
Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations. KDD 2020: 1656-1665 - [c94]Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann:
Scaling Graph Neural Networks with Approximate PageRank. KDD 2020: 2464-2473 - [c93]Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann:
From things' modeling language (ThingML) to things' machine learning (ThingML2). MODELS Companion 2020: 19:1-19:2 - [c92]Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. NeurIPS 2020 - [c91]Simon Geisler, Daniel Zügner, Stephan Günnemann:
Reliable Graph Neural Networks via Robust Aggregation. NeurIPS 2020 - [c90]Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus:
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS 2020 - [c89]Oleksandr Shchur, Nicholas Gao, Marin Bilos, Stephan Günnemann:
Fast and Flexible Temporal Point Processes with Triangular Maps. NeurIPS 2020 - [c88]Richard Leibrandt, Stephan Günnemann:
Gauss Shift: Density Attractor Clustering Faster Than Mean Shift. ECML/PKDD (1) 2020: 125-142 - [i42]Johannes Klicpera, Janek Groß, Stephan Günnemann:
Directional Message Passing for Molecular Graphs. CoRR abs/2003.03123 (2020) - [i41]Zhen Han, Yuyi Wang, Yunpu Ma, Stephan Günnemann, Volker Tresp:
Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs. CoRR abs/2003.13432 (2020) - [i40]Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. CoRR abs/2006.09239 (2020) - [i39]Oleksandr Shchur, Nicholas Gao, Marin Bilos, Stephan Günnemann:
Fast and Flexible Temporal Point Processes with Triangular Maps. CoRR abs/2006.12631 (2020) - [i38]Marcel Hildebrandt, Hang Li, Rajat Koner, Volker Tresp, Stephan Günnemann:
Scene Graph Reasoning for Visual Question Answering. CoRR abs/2007.01072 (2020) - [i37]Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann:
Scaling Graph Neural Networks with Approximate PageRank. CoRR abs/2007.01570 (2020) - [i36]Nick Harmening, Marin Bilos, Stephan Günnemann:
Deep Representation Learning and Clustering of Traffic Scenarios. CoRR abs/2007.07740 (2020) - [i35]Anna-Kathrin Kopetzki, Stephan Günnemann:
Reachable Sets of Classifiers & Regression Models: (Non-)Robustness Analysis and Robust Training. CoRR abs/2007.14120 (2020) - [i34]Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann:
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. CoRR abs/2008.12952 (2020) - [i33]Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann:
From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2). CoRR abs/2009.10632 (2020) - [i32]Armin Moin, Stephan Rössler, Stephan Günnemann:
ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning. CoRR abs/2009.10633 (2020) - [i31]Marin Bilos, Stephan Günnemann:
Equivariant Normalizing Flows for Point Processes and Sets. CoRR abs/2010.03242 (2020) - [i30]Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann:
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? CoRR abs/2010.14986 (2020) - [i29]Simon Geisler, Daniel Zügner, Stephan Günnemann:
Reliable Graph Neural Networks via Robust Aggregation. CoRR abs/2010.15651 (2020) - [i28]Johannes Klicpera, Shankari Giri, Johannes T. Margraf, Stephan Günnemann:
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules. CoRR abs/2011.14115 (2020)
2010 – 2019
- 2019
- [j14]Saskia Metzler, Stephan Günnemann, Pauli Miettinen:
Stability and dynamics of communities on online question-answer sites. Soc. Networks 58: 50-58 (2019) - [c87]Richard Kurle, Stephan Günnemann, Patrick van der Smagt:
Multi-Source Neural Variational Inference. AAAI 2019: 4114-4121 - [c86]Maximilian E. Schüle, Frédéric Simonis, Thomas Heyenbrock, Alfons Kemper, Stephan Günnemann, Thomas Neumann:
In-Database Machine Learning: Gradient Descent and Tensor Algebra for Main Memory Database Systems. BTW 2019: 247-266 - [c85]Artur Mrowca, Martin Nocker, Sebastian Steinhorst, Stephan Günnemann:
Learning Temporal Specifications from Imperfect Traces Using Bayesian Inference. DAC 2019: 96 - [c84]Maximilian E. Schüle, Dimitri Vorona, Linnea Passing, Harald Lang, Alfons Kemper, Stephan Günnemann, Thomas Neumann:
The Power of SQL Lambda Functions. EDBT 2019: 534-537 - [c83]Maximilian E. Schüle, Matthias Bungeroth, Dimitri Vorona, Alfons Kemper, Stephan Günnemann, Thomas Neumann:
ML2SQL - Compiling a Declarative Machine Learning Language to SQL and Python. EDBT 2019: 562-565 - [c82]Daniel Zügner, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Graph Neural Networks. GI-Jahrestagung 2019: 251-252 - [c81]Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann:
Predict then Propagate: Graph Neural Networks meet Personalized PageRank. ICLR (Poster) 2019 - [c80]Daniel Zügner, Stephan Günnemann:
Adversarial Attacks on Graph Neural Networks via Meta Learning. ICLR (Poster) 2019 - [c79]Aleksandar Bojchevski, Stephan Günnemann:
Adversarial Attacks on Node Embeddings via Graph Poisoning. ICML 2019: 695-704 - [c78]Daniel Zügner, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Neural Networks for Graph Data. IJCAI 2019: 6246-6250 - [c77]Daniel Zügner, Stephan Günnemann:
Certifiable Robustness and Robust Training for Graph Convolutional Networks. KDD 2019: 246-256 - [c76]Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton:
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. NeurIPS 2019: 1394-1406 - [c75]Aleksandar Bojchevski, Stephan Günnemann:
Certifiable Robustness to Graph Perturbations. NeurIPS 2019: 8317-8328 - [c74]Bertrand Charpentier, Marin Bilos, Stephan Günnemann:
Uncertainty on Asynchronous Time Event Prediction. NeurIPS 2019: 12831-12840 - [c73]Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann:
Diffusion Improves Graph Learning. NeurIPS 2019: 13333-13345 - [c72]Maximilian E. Schüle, Matthias Bungeroth, Alfons Kemper, Stephan Günnemann, Thomas Neumann:
MLearn: A Declarative Machine Learning Language for Database Systems. DEEM@SIGMOD 2019: 7:1-7:4 - [c71]Subhabrata Mukherjee, Stephan Günnemann:
GhostLink: Latent Network Inference for Influence-aware Recommendation. WWW 2019: 1310-1320 - [i27]Daniel Zügner, Stephan Günnemann:
Adversarial Attacks on Graph Neural Networks via Meta Learning. CoRR abs/1902.08412 (2019) - [i26]Subhabrata Mukherjee, Stephan Günnemann:
GhostLink: Latent Network Inference for Influence-aware Recommendation. CoRR abs/1905.05955 (2019) - [i25]Daniel Zügner, Stephan Günnemann:
Certifiable Robustness and Robust Training for Graph Convolutional Networks. CoRR abs/1906.12269 (2019) - [i24]Oleksandr Shchur, Marin Bilos, Stephan Günnemann:
Intensity-Free Learning of Temporal Point Processes. CoRR abs/1909.12127 (2019) - [i23]Oleksandr Shchur, Stephan Günnemann:
Overlapping Community Detection with Graph Neural Networks. CoRR abs/1909.12201 (2019) - [i22]Eugenio Angriman, Alexander van der Grinten, Aleksandar Bojchevski, Daniel Zügner, Stephan Günnemann, Henning Meyerhenke:
Group Centrality Maximization for Large-scale Graphs. CoRR abs/1910.13874 (2019) - [i21]Aleksandar Bojchevski, Stephan Günnemann:
Certifiable Robustness to Graph Perturbations. CoRR abs/1910.14356 (2019) - [i20]Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann:
Diffusion Improves Graph Learning. CoRR abs/1911.05485 (2019) - [i19]Marin Bilos, Bertrand Charpentier, Stephan Günnemann:
Uncertainty on Asynchronous Time Event Prediction. CoRR abs/1911.05503 (2019) - [i18]Alexander Ziller, Julius Hansjakob, Vitalii Rusinov, Daniel Zügner, Peter Vogel, Stephan Günnemann:
Oktoberfest Food Dataset. CoRR abs/1912.05007 (2019) - 2018
- [c70]Aleksandar Bojchevski, Stephan Günnemann:
Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure. AAAI 2018: 2738-2745 - [c69]Oleksandr Shchur, Aleksandar Bojchevski, Mohamed Farghal, Stephan Günnemann, Yusuf Saber:
Anomaly Detection in Car-Booking Graphs. ICDM Workshops 2018: 604-607 - [c68]Aleksandar Bojchevski, Stephan Günnemann:
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. ICLR (Poster) 2018 - [c67]Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann:
NetGAN: Generating Graphs via Random Walks. ICML 2018: 609-618 - [c66]Peter Wolf, Artur Mrowca, Tam Thanh Nguyen, Bernard Bäker, Stephan Günnemann:
Pre-ignition Detection Using Deep Neural Networks: A Step Towards Data-driven Automotive Diagnostics. ITSC 2018: 176-183 - [c65]Daniel Zügner, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Neural Networks for Graph Data. KDD 2018: 2847-2856 - [c64]Armin Moin, Stephan Rössler, Stephan Günnemann:
ThingML+: Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning. MODELS Workshops 2018: 521-523 - [c63]Artur Mrowca, Barbara Moser, Stephan Günnemann:
Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping. ECML/PKDD (3) 2018: 86-102 - [c62]Marawan Shalaby, Jan Stutzki, Matthias Schubert, Stephan Günnemann:
An LSTM Approach to Patent Classification based on Fixed Hierarchy Vectors. SDM 2018: 495-503 - [c61]Richard Leibrandt, Stephan Günnemann:
Making Kernel Density Estimation Robust towards Missing Values in Highly Incomplete Multivariate Data without Imputation. SDM 2018: 747-755 - [c60]Lorenzo von Ritter, Michael E. Houle, Stephan Günnemann:
Intrinsic Degree: An Estimator of the Local Growth Rate in Graphs. SISAP 2018: 195-208 - [i17]Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann:
NetGAN: Generating Graphs via Random Walks. CoRR abs/1803.00816 (2018) - [i16]Daniel Zügner, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Neural Networks for Graph Data. CoRR abs/1805.07984 (2018) - [i15]Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, Michael M. Bronstein:
Dual-Primal Graph Convolutional Networks. CoRR abs/1806.00770 (2018) - [i14]Aleksandar Bojchevski, Stephan Günnemann:
Adversarial Attacks on Node Embeddings. CoRR abs/1809.01093 (2018) - [i13]Roberto Alonso, Stephan Günnemann:
Mining Contrasting Quasi-Clique Patterns. CoRR abs/1810.01836 (2018) - [i12]Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann:
Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank. CoRR abs/1810.05997 (2018) - [i11]Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton:
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. CoRR abs/1810.11953 (2018) - [i10]Richard Kurle, Stephan Günnemann, Patrick van der Smagt:
Multi-Source Neural Variational Inference. CoRR abs/1811.04451 (2018) - [i9]Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann:
Pitfalls of Graph Neural Network Evaluation. CoRR abs/1811.05868 (2018) - 2017
- [j13]Stephan Günnemann:
Machine Learning Meets Databases. Datenbank-Spektrum 17(1): 77-83 (2017) - [j12]Manuel Then, Stephan Günnemann, Alfons Kemper, Thomas Neumann:
Efficient Batched Distance, Closeness and Betweenness Centrality Computation in Unweighted and Weighted Graphs. Datenbank-Spektrum 17(2): 169-182 (2017) - [j11]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl:
MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels. Knowl. Inf. Syst. 50(2): 417-446 (2017) - [j10]Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos, Disha Makhija, Mohit Kumar:
ZooBP: Belief Propagation for Heterogeneous Networks. Proc. VLDB Endow. 10(5): 625-636 (2017) - [j9]Manuel Then, Timo Kersten, Stephan Günnemann, Alfons Kemper, Thomas Neumann:
Automatic Algorithm Transformation for Efficient Multi-Snapshot Analytics on Temporal Graphs. Proc. VLDB Endow. 10(8): 877-888 (2017) - [c59]Manuel Then, Stephan Günnemann, Alfons Kemper, Thomas Neumann:
Efficient Batched Distance and Centrality Computation in Unweighted and Weighted Graphs. BTW 2017: 247-266 - [c58]Linnea Passing, Manuel Then, Nina C. Hubig, Harald Lang, Michael Schreier, Stephan Günnemann, Alfons Kemper, Thomas Neumann:
SQL- and Operator-centric Data Analytics in Relational Main-Memory Databases. EDBT 2017: 84-95 - [c57]Aleksandar Bojchevski, Yves Matkovic, Stephan Günnemann:
Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings. KDD 2017: 737-746 - [c56]Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos:
The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation. SDM 2017: 144-152 - [c55]Nina C. Hubig, Philip Fengler, Andreas Züfle, Ruixin Yang, Stephan Günnemann:
Detection and Prediction of Natural Hazards Using Large-Scale Environmental Data. SSTD 2017: 300-316 - [i8]Subhabrata Mukherjee, Stephan Günnemann, Gerhard Weikum:
Personalized Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion. CoRR abs/1705.02669 (2017) - [i7]Aleksandar Bojchevski, Stephan Günnemann:
Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking. CoRR abs/1707.03815 (2017) - [i6]Stephan Rabanser, Oleksandr Shchur, Stephan Günnemann:
Introduction to Tensor Decompositions and their Applications in Machine Learning. CoRR abs/1711.10781 (2017) - 2016
- [j8]Miguel Araujo, Stephan Günnemann, Spiros Papadimitriou, Christos Faloutsos, Prithwish Basu, Ananthram Swami, Evangelos E. Papalexakis, Danai Koutra:
Discovery of "comet" communities in temporal and labeled graphs Com^2. Knowl. Inf. Syst. 46(3): 657-677 (2016) - [c54]Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Günnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos:
EdgeCentric: Anomaly Detection in Edge-Attributed Networks. ICDM Workshops 2016: 327-334 - [c53]Saskia Metzler, Stephan Günnemann, Pauli Miettinen:
Hyperbolae are No Hyperbole: Modelling Communities That are Not Cliques. ICDM 2016: 330-339 - [c52]Subhabrata Mukherjee, Stephan Günnemann, Gerhard Weikum:
Continuous Experience-aware Language Model. KDD 2016: 1075-1084 - [c51]Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos:
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. SDM 2016: 495-503 - [i5]Saskia Metzler, Stephan Günnemann, Pauli Miettinen:
Hyperbolae Are No Hyperbole: Modelling Communities That Are Not Cliques. CoRR