Data Mining & Analytics

Headed by Rudolf Mößbauer Tenure Track Professor Stephan Günnemann, the focus group "Data Mining and Analytics" explores the development of robust machine learning methods, with major focus on mining and learning principles for graphs and networks.

Since in many real-world applications the collected data is rarely of high-quality but often noisy, prone to errors, or vulnerable to manipulations, robustness of algorithms is crucial to ensure reliable results. Therefore, the group's goal is to design techniques which handle different forms of errors and corruptions in an automatic way. In this regard, the focus group is especially interested in designing techniques for non-independent data: While one of the most common assumptions in many machine learning and data analysis tasks is that the given data points are realizations of independent and identically distributed random variables, this assumption is often violated. Sensors are interlinked with each other in networked cyber physical systems, people exchange information in social networks, and molecules or proteins interact based on biochemical events. To tackle these challenges, the focus group develops learning methods for, e.g., graphs and network data.

Publications by the Focus Group

2019

  • Metzler, Saskia; Günnemann, Stephan; Miettinen, Pauli: Stability and dynamics of communities on online question–answer sites. Social Networks 58, 2019, 50-58 more…
  • Mrowca, Artur; Moser, Barbara; Günnemann, Stephan: Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping. In: Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2019 more…

2018

  • Bojchevski, Aleksandar; Günnemann, Stephan: Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure. AAAI Conference on Artificial Intelligence, 2018, 2738-2745 more…
  • Bojchevski, Aleksandar; Günnemann, Stephan: Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. International Conference on Learning Representations, 2018, 1–13 more…
  • Bojchevski, Aleksandar; Shchur, Oleksandr; Zügner, Daniel; Günnemann,Stephan: NetGAN: Generating Graphs via Random Walks. International Conference on Machine Learning, 2018, 609–618 more…
  • Klicpera, Johannes; Bojchevski, Aleksandar; Günnemann, Stephan: Predict then Propagate: Combining neural networks with personalized pagerank for classification on graphs. International Conference on Learning Representations, 2018 more…
  • Kurle, Richard; Günnemann, Stephan; van der Smagt, Patrick: Multi-Source Neural Variational Inference. AAAI 2019, Association for the Advancement of Artificial Intelligence, 2018 more…
  • Leibrandt, Richard; Günnemann, Stephan: Making Kernel Density Estimation Robust towards Missing Values in Highly Incomplete Multivariate Data without Imputation. In: Proceedings of the 2018 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2018 more…
  • Shalaby, Marawan; Stutzki, Jan; Schubert, Matthias; Günnemann, Stephan: An LSTM Approach to Patent Classification based on Fixed Hierarchy Vectors. In: Proceedings of the 2018 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2018 more…
  • Wolf, Peter; Mrowca, Artur; Nguyen, Tam Thanh; Baker, Bernard; Gunnemann, Stephan: Pre-ignition Detection Using Deep Neural Networks: A Step Towards Data-driven Automotive Diagnostics. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2018 more…
  • Zügner, Daniel; Akbarnejad, Amir; Günnemann, Stephan: Adversarial Attacks on Neural Networks for Graph Data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18, ACM Press, 2018 more…
  • von Ritter, Lorenzo; Houle, Michael E.; Günnemann, Stephan: Intrinsic Degree: An Estimator of the Local Growth Rate in Graphs. In: Similarity Search and Applications. Springer International Publishing, 2018 more…

2017

  • A. Bojchevski, and S. Günnemann: Bayesian robust attributed graph clustering: joint learning of partial anomalies and group structure,. AAAI Conf. Artificial Intelligence, 2017 more…
  • Bojchevski, Aleksandar; Matkovic, Yves; Günnemann, Stephan: Robust Spectral Clustering for Noisy Data. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17, ACM Press, 2017 more…
  • Eswaran, Dhivya; Günnemann, Stephan; Faloutsos, Christos: The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation. In: Proceedings of the 2017 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2017 more…
  • Eswaran, Dhivya; Günnemann, Stephan; Faloutsos, Christos; Makhija, Disha; Kumar, Mohit: ZooBP. Proceedings of the VLDB Endowment 10 (5), 2017, 625-636 more…
  • Günnemann, Stephan: Machine Learning Meets Databases. Datenbank-Spektrum 17 (1), 2017, 77-83 more…
  • Hubig, Nina; Fengler, Philip; Züfle, Andreas; Yang, Ruixin; Günnemann, Stephan: Detection and Prediction of Natural Hazards Using Large-Scale Environmental Data. In: Advances in Spatial and Temporal Databases. Springer International Publishing, 2017 more…
  • M. Then, S. Günnemann, A. Kemper, and T. Neumann: Efficient batched distance and centrality computation in unweighted and weighted graphs. Conference on Database Systems for Business, Technology, and Web, 2017 more…
  • Passing, Linnea; Then, Manuel; Hubig, Nina; Lang, Harald; Schreier, Michael; Günnemann, Stephan; Kemper, Alfons; Neumann, Thomas: SQL- and Operator-centric Data Analytics in Relational Main-Memory Databases. 2017, 10.5441/002/edbt.2017.09(other entry) more…
  • Then, Manuel; Günnemann, Stephan; Kemper, Alfons; Neumann, Thomas: Efficient Batched Distance, Closeness and Betweenness Centrality Computation in Unweighted and Weighted Graphs. Datenbank-Spektrum 17 (2), 2017, 169-182 more…
  • Then, Manuel; Kersten, Timo; Günnemann, Stephan; Kemper, Alfons; Neumann, Thomas: Automatic algorithm transformation for efficient multi-snapshot analytics on temporal graphs. Proceedings of the VLDB Endowment 10 (8), 2017, 877-888 more…

2016

  • Boden, Brigitte; Günnemann, Stephan; Hoffmann, Holger; Seidl, Thomas: MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels. Knowledge and Information Systems 50 (2), 2016, 417-446 more…