Publications

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Preprint

  • Hoppe, J., Grande, V. P., & Schaub, M. T. (2025). Don’t be Afraid of Cell Complexes! An Introduction from an Applied Perspective (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2506.09726
  • Savostianov, A., Schaub, M. T., Guglielmi, N., & Tudisco, F. (2025). Efficient Sparsification of Simplicial Complexes via Local Densities of States (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2502.07558
  • Hoppe, J., Grande, V. P., & Schaub, M. T. (2025). Don’t be Afraid of Cell Complexes! An Introduction from an Applied Perspective. https://arxiv.org/abs/2506.09726
    [PDF]
  • Cheng, M., Jansen, J., Reimer, K., Grande, V., Nagai, J. S., Li, Z., Kießling, P., Grasshoff, M., Kuppe, C., Schaub, M. T., Kramann, R., & Costa, I. G. (2024). PHLOWER - Single cell trajectory analysis using Hodge Decomposition. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2024.10.01.613179
  • Grande, V. P., & Schaub, M. T. (2024). Point-Level Topological Representation Learning on Point Clouds (Version 3). arXiv. https://doi.org/10.48550/ARXIV.2406.02300
  • Hoppe, J., & Schaub, M. T. (2024). Random Abstract Cell Complexes (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2406.01999
  • Telyatnikov, L., Bernardez, G., Montagna, M., Hajij, M., Carrasco, M., Vasylenko, P., Papillon, M., Zamzmi, G., Schaub, M. T., Verhellen, J., Snopov, P., Miquel-Oliver, B., Gil-Sorribes, M., Molina, A., Guallar, V., Long, T., Suk, J., Rygiel, P., Nikitin, A., … Papamarkou, T. (2024). TopoBench: A Framework for Benchmarking Topological Deep Learning (Version 3). arXiv. https://doi.org/10.48550/ARXIV.2406.06642
  • Hajij, M., Zamzmi, G., Papamarkou, T., Miolane, N., Guzmán-Sáenz, A., Ramamurthy, K. N., Birdal, T., Dey, T. K., Mukherjee, S., Samaga, S. N., Livesay, N., Walters, R., Rosen, P., & Schaub, M. T. (2022). Topological Deep Learning: Going Beyond Graph Data (Version 3). arXiv. https://doi.org/10.48550/ARXIV.2206.00606

2025

  • Frantzen, F., & Schaub, M. T. (2025). HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection. Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, 626–636. https://doi.org/10.1145/3711896.3736998
    [Code]
  • Rompelberg, L., & Schaub, M. T. (2025). A Bayesian Perspective on Uncertainty Quantification for Estimated Graph Signals. ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. https://doi.org/10.1109/icassp49660.2025.10889783
    [PDF] [arXiv]
  • Spreuer, T., Hoppe, J., & Schaub, M. T. (2025). Faster Inference of Cell Complexes from Flows via Matrix Factorization.
    [PDF] [Slides] [Code]

2024

  • Papamarkou, T., Birdal, T., Bronstein, M. M., Carlsson, G. E., Curry, J., Gao, Y., Hajij, M., Kwitt, R., Lio, P., Di Lorenzo, P., Maroulas, V., Miolane, N., Nasrin, F., Natesan Ramamurthy, K., Rieck, B., Scardapane, S., Schaub, M. T., Veličković, P., Wang, B., … Zamzmi, G. (2024). Position: Topological Deep Learning is the New Frontier for Relational Learning. In R. Salakhutdinov, Z. Kolter, K. Heller, A. Weller, N. Oliver, J. Scarlett, & F. Berkenkamp (Eds.), Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 39529–39555). PMLR. https://proceedings.mlr.press/v235/papamarkou24a.html
    [PDF]
  • Neuhäuser, L., Scholkemper, M., Tudisco, F., & Schaub, M. T. (2024). Learning the effective order of a hypergraph dynamical system. Science Advances, 10(19). https://doi.org/10.1126/sciadv.adh4053
  • Grande, V. P., & Schaub, M. T. (2024). Disentangling the Spectral Properties of the Hodge Laplacian: not all small Eigenvalues are Equal. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 9896–9900. https://doi.org/10.1109/icassp48485.2024.10446051
  • Grande, V. P., & Schaub, M. T. (2024). Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH. In S. Villar & B. Chamberlain (Eds.), Proceedings of the Second Learning on Graphs Conference (Vol. 231, p. 17:1-17:19). PMLR. https://proceedings.mlr.press/v231/grande24a.html
    [PDF]
  • Hoppe, J., & Schaub, M. T. (2024). Representing Edge Flows on Graphs via Sparse Cell Complexes. In S. Villar & B. Chamberlain (Eds.), Proceedings of the Second Learning on Graphs Conference (Vol. 231, p. 1:1-1:22). PMLR. https://proceedings.mlr.press/v231/hoppe24a.html
  • Savostianov, A., Tudisco, F., & Guglielmi, N. (2024). Cholesky-like Preconditioner for Hodge Laplacians via Heavy Collapsible Subcomplex. SIAM Journal on Matrix Analysis and Applications, 45(4), 1827–1849. https://doi.org/10.1137/23m1626396
    [PDF] [Code]
  • Grande, V. P., Hoppe, J., Frantzen, F., & Schaub, M. T. (2024). Topological Trajectory Classification and Landmark Inference on Simplicial Complexes. 2024 58th Asilomar Conference on Signals, Systems, and Computers, 44–48. https://doi.org/10.1109/ieeeconf60004.2024.10942887
  • Epping, B., René, A., Helias, M., & Schaub, M. T. (2024). Graph Neural Networks Do Not Always Oversmooth. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), Advances in Neural Information Processing Systems (Vol. 37, pp. 48164–48188). Curran Associates, Inc.
    [PDF] [Code]
  • Frantzen, F., & Schaub, M. T. (2024). Learning From Simplicial Data Based on Random Walks and 1D Convolutions. The Twelfth International Conference on Learning Representations.
    [PDF] [Code]
  • Hajij, M., Papillon, M., Frantzen, F., Agerberg, J., AlJabea, I., Ballester, R., Battiloro, C., Bernárdez, G., Birdal, T., Brent, A., Chin, P., Escalera, S., Fiorellino, S., Gardaa, O. H., Gopalakrishnan, G., Govil, D., Hoppe, J., Karri, M. R., Khouja, J., … Miolane, N. (2024). TopoX: A Suite of Python Packages for Machine Learning on Topological Domains. Journal of Machine Learning Research, 25(374), 1–8. http://jmlr.org/papers/v25/24-0110.html
    [PDF] [GitHub]

2023

  • Grande, V. P., & Schaub, M. T. (2023). Topological Point Cloud Clustering. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 11683–11697). PMLR. https://proceedings.mlr.press/v202/grande23a.html
    [PDF]
  • Roddenberry, T. M., Grande, V. P., Frantzen, F., Schaub, M. T., & Segarra, S. (2023). Signal Processing On Product Spaces. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. https://doi.org/10.1109/icassp49357.2023.10095735
  • Hajij, M., Zamzmi, G., Papamarkou, T., Guzman-Saenz, Ai., Birdal, T., & Schaub, M. T. (2023). Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs. 2023 57th Asilomar Conference on Signals, Systems, and Computers, 799–803. https://doi.org/10.1109/ieeeconf59524.2023.10477018

2022

  • Roddenberry, T. M., Frantzen, F., Schaub, M. T., & Segarra, S. (2022). Hodgelets: Localized Spectral Representations of Flows On Simplicial Complexes. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5922–5926. https://doi.org/10.1109/icassp43922.2022.9747203
    [Code]
  • Schaub, M. T., Seby, J.-B., Frantzen, F., Roddenberry, T. M., Zhu, Y., & Segarra, S. (2022). Signal Processing on Simplicial Complexes. In Understanding Complex Systems (pp. 301–328). Springer International Publishing. https://doi.org/10.1007/978-3-030-91374-8_12

2021