Computational Network Science
The Computational Network Science group headed by Michael Schaub is part of the Computer Science Department. The research interests of our group are broad, but have one common denominator: the use of networks to analyse a variety of systems and data.
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M. Scholkemper and M. T. Schaub, "Local, Global And Scale-Dependent Node Roles," 2021 IEEE International Conference on Autonomous Systems (ICAS), Montreal, QC, Canada, 2021, pp. 1-5, doi: 10.1109/ICAS49788.2021.9551110.
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F. Frantzen, J.-B. Seby and M. T. Schaub, "Outlier Detection for Trajectories via Flow-embeddings," 2021 55th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2021, pp. 1568-1572, doi: 10.1109/IEEECONF53345.2021.9723128.
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M. Scholkemper and M. T. Schaub, "Blind Extraction of Equitable Partitions from Graph Signals," ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, Singapore, 2022, pp. 5832-5836, doi: 10.1109/ICASSP43922.2022.9746676.
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"Improving the visibility of minorities through network growth interventions", Communication Physics 6, 108 (2023), doi:10.1038/s42005-023-01218-9
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"An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions", Michael Scholkemper and Michael T. Schaub, (2023), NeurIPS23.
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Javier Esparza, Vincent P. Grande: "Black-Box Testing Liveness Properties of Partially Observable Stochastic Systems", ICALP 2023. 50th EATS International Colloquium on Automata, Languages and Programming, 2023
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Stamm FI, Scholkemper M, Strohmaier M, Schaub MT. Neighborhood structure configuration models. InProceedings of the ACM Web Conference 2023 2023 Apr 30 (pp. 210-220).
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"Non-isotropic Persistent Homology: Leveraging the Metric Dependency of PH", Vincent P. Grande and Michael T. Schaub, Proceedings of the 2nd Annual Learning on Graphs Conference.
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"Representing Edge Flows on Graphs via Sparse Cell Complexes", Learning on Graphs (LoG) 2023; see also https://github.com/josefhoppe/edge-flow-cell-complexes
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"Topological Point Cloud Clustering", Vincent P. Grande and Michael T. Schaub, 40th International Conference on Machine Learning 2023.
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Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal, Vincent P. Grande and Michael T. Schaub, International Conference on Acoustics, Speech, and Signal Processing 2024. https://arxiv.org/abs/2311.14427
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"Learning From Simplicial Data Based on Random Walks and 1D Convolutions", Florian Frantzen and Michael T. Schaub, The Twelfth International Conference on Learning Representations, 2024
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Code for "Topological Trajectory Classification and Landmark Inference on Simplicial Complexes", Vincent P. Grande, Josef Hoppe, Florian Frantzen, Michael T. Schaub, 2024 Asilomar Conference on Signals, Systems, and Computers, IEEE.
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"A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings", Scholkemper, M., Kühn, D., Nabbefeld, G., Musall, S., Kampa, B., & Schaub, M. T. (2024)