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Michael T. Schaub
Person information
- affiliation: RWTH Aachen University, Department of Computer Science, Aachen, Germany
- affiliation (former): University of Oxford, UK
- affiliation (former): Massachusetts Institute of Technology (MIT), Institute for Data, Systems, and Society, Cambridge, MA, USA
- affiliation (former): Université catholique de Louvain, Louvain-la-Neuve, Belgium
- affiliation (former): University of Namur (UNamur), Belgium
- affiliation (former, PhD): Imperial College London, UK
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2020 – today
- 2024
- [j22]Alexis Arnaudon
, Dominik J. Schindler
Algorithm 1044: PyGenStability, a Multiscale Community Detection Framework with Generalized Markov Stability. ACM Trans. Math. Softw. 50(2): 15:1-15:8 (2024) - [c26]Michael Scholkemper
A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings. ICASSP 2024: 9751-9755 - [c25]James Shiniti Nagai, Ivan G. Costa, Michael T. Schaub:
Optimal Transport Distances for Directed, Weighted Graphs: A Case Study With Cell-Cell Communication Networks. ICASSP 2024: 9856-9860 - [c24]Vincent P. Grande, Michael T. Schaub:
Disentangling the Spectral Properties of the Hodge Laplacian: not all small Eigenvalues are Equal. ICASSP 2024: 9896-9900 - [c23]Florian Frantzen, Michael T. Schaub:
Learning From Simplicial Data Based on Random Walks and 1D Convolutions. ICLR 2024 - [c22]Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi:
Position: Topological Deep Learning is the New Frontier for Relational Learning. ICML 2024 - [i67]Felix I. Stamm, Michael T. Schaub:
Faster optimal univariate microgaggregation. CoRR abs/2401.02381 (2024) - [i66]Michael Scholkemper, Damin Kühn, Gerion Nabbefeld, Simon Musall, Björn Kampa, Michael T. Schaub:
A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings. CoRR abs/2401.03913 (2024) - [i65]Mustafa Hajij, Mathilde Papillon, Florian Frantzen
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains. CoRR abs/2402.02441 (2024) - [i64]Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck
Position Paper: Challenges and Opportunities in Topological Deep Learning. CoRR abs/2402.08871 (2024) - [i63]Michaël Fanuel, Antoine Aspeel, Michael T. Schaub, Jean-Charles Delvenne:
Ellipsoidal embeddings of graphs. CoRR abs/2403.15023 (2024) - [i62]Florian Frantzen
Learning From Simplicial Data Based on Random Walks and 1D Convolutions. CoRR abs/2404.03434 (2024) - [i61]Josef Hoppe, Michael T. Schaub:
Random Abstract Cell Complexes. CoRR abs/2406.01999 (2024) - [i60]Bastian Epping, Alexandre René, Moritz Helias, Michael T. Schaub:
Graph Neural Networks Do Not Always Oversmooth. CoRR abs/2406.02269 (2024) - [i59]Vincent P. Grande, Michael T. Schaub:
Node-Level Topological Representation Learning on Point Clouds. CoRR abs/2406.02300 (2024) - [i58]Michael Scholkemper, Xinyi Wu, Ali Jadbabaie, Michael T. Schaub:
Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs. CoRR abs/2406.02997 (2024) - [i57]Lev Telyatnikov, Guillermo Bernárdez, Marco Montagna, Pavlo Vasylenko, Ghada Zamzmi, Mustafa Hajij, Michael T. Schaub, Nina Miolane, Simone Scardapane, Theodore Papamarkou:
TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning. CoRR abs/2406.06642 (2024) - 2023
- [j21]Christian Bick
What Are Higher-Order Networks? SIAM Rev. 65(3): 686-731 (2023) - [c21]Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, AIdo Guzman-Saenz, Tolga Birdal, Michael T. Schaub:
Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs. ACSSC 2023: 799-803 - [c20]T. Mitchell Roddenberry, Vincent P. Grande, Florian Frantzen
Signal Processing On Product Spaces. ICASSP 2023: 1-5 - [c19]Vincent Peter Grande, Michael T. Schaub:
Topological Point Cloud Clustering. ICML 2023: 11683-11697 - [c18]Josef Hoppe, Michael T. Schaub:
Representing Edge Flows on Graphs via Sparse Cell Complexes. LoG 2023: 1 - [c17]Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra:
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. LoG 2023: 6 - [c16]Vincent Peter Grande, Michael T. Schaub:
Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH. LoG 2023: 17 - [c15]Michael Scholkemper, Michael T. Schaub:
An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions. NeurIPS 2023 - [c14]Mathilde Papillon, Mustafa Hajij, Audun Myers, Florian Frantzen, Ghada Zamzmi, Helen Jenne, Johan Mathe, Josef Hoppe, Michael T. Schaub, Theodore Papamarkou
ICML 2023 Topological Deep Learning Challenge: Design and Results. TAG-ML 2023: 3-8 - [c13]Felix I. Stamm
Neighborhood Structure Configuration Models. WWW 2023: 210-220 - [i56]Lucille Calmon, Michael T. Schaub, Ginestra Bianconi:
Dirac signal processing of higher-order topological signals. CoRR abs/2301.10137 (2023) - [i55]Alexis Arnaudon
PyGenStability: Multiscale community detection with generalized Markov Stability. CoRR abs/2303.05385 (2023) - [i54]Vincent P. Grande, Michael T. Schaub:
Topological Point Cloud Clustering. CoRR abs/2303.16716 (2023) - [i53]Michael Scholkemper, Michael T. Schaub:
An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions. CoRR abs/2305.19087 (2023) - [i52]Leonie Neuhäuser, Michael Scholkemper, Francesco Tudisco, Michael T. Schaub:
Learning the effective order of a hypergraph dynamical system. CoRR abs/2306.01813 (2023) - [i51]Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra:
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. CoRR abs/2306.05557 (2023) - [i50]Josef Hoppe, Michael T. Schaub:
Representing Edge Flows on Graphs via Sparse Cell Complexes. CoRR abs/2309.01632 (2023) - [i49]James Shiniti Nagai, Ivan G. Costa, Michael T. Schaub:
Optimal transport distances for directed, weighted graphs: a case study with cell-cell communication networks. CoRR abs/2309.07030 (2023) - [i48]Mathilde Papillon, Mustafa Hajij, Florian Frantzen, Josef Hoppe, Helen Jenne, Johan Mathe, Audun Myers, Theodore Papamarkou, Michael T. Schaub, Ghada Zamzmi, Tolga Birdal, Tamal K. Dey, Tim Doster, Tegan Emerson, Gurusankar Gopalakrishnan, Devendra Govil, Vincent P. Grande, Aldo Guzmán-Sáenz, Henry Kvinge, Neal Livesay, Jan Meissner, Soham Mukherjee, Shreyas N. Samaga, Karthikeyan Natesan Ramamurthy, Maneel Reddy Karri, Paul Rosen, Sophia Sanborn
ICML 2023 Topological Deep Learning Challenge : Design and Results. CoRR abs/2309.15188 (2023) - [i47]Vincent P. Grande, Michael T. Schaub:
Non-isotropic Persistent Homology: Leveraging the Metric Dependency of PH. CoRR abs/2310.16437 (2023) - [i46]Vincent P. Grande, Michael T. Schaub:
Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal. CoRR abs/2311.14427 (2023) - [i45]Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Aldo Guzmán-Sáenz, Tolga Birdal, Michael T. Schaub:
Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs. CoRR abs/2312.09504 (2023) - 2022
- [j20]Florian Klimm
Modularity Maximization for Graphons. SIAM J. Appl. Math. 82(6): 1930-1952 (2022) - [j19]Maosheng Yang
Simplicial Convolutional Filters. IEEE Trans. Signal Process. 70: 4633-4648 (2022) - [c12]Lucille Calmon
Higher-order signal processing with the Dirac operator. IEEECONF 2022: 925-929 - [c11]Michael Scholkemper
Blind Extraction of Equitable Partitions from Graph Signals. ICASSP 2022: 5832-5836 - [c10]T. Mitchell Roddenberry, Florian Frantzen
Hodgelets: Localized Spectral Representations of Flows On Simplicial Complexes. ICASSP 2022: 5922-5926 - [c9]T. Mitchell Roddenberry, Michael T. Schaub, Mustafa Hajij:
Signal Processing On Cell Complexes. ICASSP 2022: 8852-8856 - [c8]Jiong Zhu, Junchen Jin, Donald Loveland, Michael T. Schaub, Danai Koutra:
How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications. KDD 2022: 2637-2647 - [i44]Maosheng Yang, Elvin Isufi, Michael T. Schaub, Geert Leus:
Simplicial Convolutional Filters. CoRR abs/2201.11720 (2022) - [i43]Michael Scholkemper, Michael T. Schaub:
Blind Extraction of Equitable Partitions from Graph Signals. CoRR abs/2203.05407 (2022) - [i42]Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra:
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. CoRR abs/2207.04376 (2022) - [i41]Leonie Neuhäuser, Fariba Karimi, Jan Bachmann, Markus Strohmaier, Michael T. Schaub:
Improving the visibility of minorities through network growth interventions. CoRR abs/2208.03263 (2022) - [i40]Felix I. Stamm, Michael Scholkemper, Markus Strohmaier, Michael T. Schaub:
Neighborhood Structure Configuration Models. CoRR abs/2210.06843 (2022) - [i39]Lucille Calmon, Michael T. Schaub, Ginestra Bianconi:
Higher-order signal processing with the Dirac operator. CoRR abs/2212.10196 (2022) - 2021
- [j18]Leonie Neuhäuser
Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes. Appl. Netw. Sci. 6(1): 86 (2021) - [j17]James Shiniti Nagai
CrossTalkeR: analysis and visualization of ligand-receptorne tworks. Bioinform. 37(22): 4263-4265 (2021) - [j16]Michael T. Schaub
Signal processing on higher-order networks: Livin' on the edge... and beyond. Signal Process. 187: 108149 (2021) - [c7]Florian Frantzen
Outlier Detection for Trajectories via Flow-embeddings. ACSCC 2021: 1568-1572 - [c6]Maosheng Yang
Finite Impulse Response Filters for Simplicial Complexes. EUSIPCO 2021: 2005-2009 - [i38]Florian Klimm, Nick S. Jones, Michael T. Schaub:
Modularity maximisation for graphons. CoRR abs/2101.00503 (2021) - [i37]Michael T. Schaub, Yu Zhu, Jean-Baptiste Seby, T. Mitchell Roddenberry, Santiago Segarra:
Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond. CoRR abs/2101.05510 (2021) - [i36]Maosheng Yang, Elvin Isufi, Michael T. Schaub, Geert Leus:
Finite Impulse Response Filters for Simplicial Complexes. CoRR abs/2103.12587 (2021) - [i35]Christian Bick, Elizabeth Gross, Heather A. Harrington, Michael T. Schaub:
What are higher-order networks? CoRR abs/2104.11329 (2021) - [i34]Leonie Neuhäuser
Consensus Dynamics and Opinion Formation on Hypergraphs. CoRR abs/2105.01369 (2021) - [i33]Michael Scholkemper, Michael T. Schaub:
Local, global and scale-dependent node roles. CoRR abs/2105.12598 (2021) - [i32]Michael T. Schaub, Jean-Baptiste Seby, Florian Frantzen, T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra:
Signal processing on simplicial complexes. CoRR abs/2106.07471 (2021) - [i31]Jiong Zhu, Junchen Jin, Michael T. Schaub, Danai Koutra:
Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs. CoRR abs/2106.07767 (2021) - [i30]Leonie Neuhäuser
Consensus dynamics on temporal hypergraphs. CoRR abs/2109.04985 (2021) - [i29]T. Mitchell Roddenberry, Florian Frantzen, Michael T. Schaub, Santiago Segarra:
Hodgelets: Localized Spectral Representations of Flows on Simplicial Complexes. CoRR abs/2109.08728 (2021) - [i28]T. Mitchell Roddenberry, Michael T. Schaub, Mustafa Hajij:
Signal Processing on Cell Complexes. CoRR abs/2110.05614 (2021) - [i27]Florian Frantzen, Jean-Baptiste Seby, Michael T. Schaub:
Outlier Detection for Trajectories via Flow-embeddings. CoRR abs/2111.13235 (2021) - 2020
- [j15]Michael T. Schaub
Random Walks on Simplicial Complexes and the Normalized Hodge 1-Laplacian. SIAM Rev. 62(2): 353-391 (2020) - [j14]Michael T. Schaub
Blind Identification of Stochastic Block Models from Dynamical Observations. SIAM J. Math. Data Sci. 2(2): 335-367 (2020) - [j13]Marco Avella-Medina
Centrality Measures for Graphons: Accounting for Uncertainty in Networks. IEEE Trans. Netw. Sci. Eng. 7(1): 520-537 (2020) - [j12]Yu Zhu
Network Inference From Consensus Dynamics With Unknown Parameters. IEEE Trans. Signal Inf. Process. over Networks 6: 300-315 (2020) - [j11]T. Mitchell Roddenberry
Exact Blind Community Detection From Signals on Multiple Graphs. IEEE Trans. Signal Process. 68: 5016-5030 (2020) - [c5]Leonie Neuhäuser
Opinion Dynamics with Multi-body Interactions. NetGCooP 2020: 261-271 - [i26]T. Mitchell Roddenberry, Michael T. Schaub, Hoi-To Wai, Santiago Segarra:
Exact Blind Community Detection from Signals on Multiple Graphs. CoRR abs/2001.10944 (2020) - [i25]Michael T. Schaub, Leto Peel:
Hierarchical community structure in networks. CoRR abs/2009.07196 (2020) - [i24]Leto Peel, Michael T. Schaub:
Detectability of hierarchical communities in networks. CoRR abs/2009.07525 (2020) - [i23]Felix I. Stamm, Leonie Neuhäuser
Systematic edge uncertainty in attributed social networks and its effects on rankings of minority nodes. CoRR abs/2010.11546 (2020)
2010 – 2019
- 2019
- [c4]Michael T. Schaub
Spectral Partitioning of Time-varying Networks with Unobserved Edges. ICASSP 2019: 4938-4942 - [c3]Junteng Jia, Michael T. Schaub
Graph-based Semi-Supervised & Active Learning for Edge Flows. KDD 2019: 761-771 - [i22]Michael T. Schaub, Santiago Segarra, Hoi-To Wai:
Spectral partitioning of time-varying networks with unobserved edges. CoRR abs/1904.11930 (2019) - [i21]Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson:
Graph-based Semi-Supervised & Active Learning for Edge Flows. CoRR abs/1905.07451 (2019) - [i20]Michael T. Schaub, Santiago Segarra, John N. Tsitsiklis:
Blind identification of stochastic block models from dynamical observations. CoRR abs/1905.09107 (2019) - [i19]Yu Zhu, Michael T. Schaub, Ali Jadbabaie, Santiago Segarra:
Network Inference from Consensus Dynamics with Unknown Parameters. CoRR abs/1908.01393 (2019) - 2018
- [j10]Ernesto Estrada
Random multi-hopper model: super-fast random walks on graphs. J. Complex Networks 6(3): 382-403 (2018) - [j9]Mauro Faccin
Entrograms and coarse graining of dynamics on complex networks. J. Complex Networks 6(5): 661-678 (2018) - [j8]Yazan N. Billeh
Feedforward architectures driven by inhibitory interactions. J. Comput. Neurosci. 44(1): 63-74 (2018) - [j7]Austin R. Benson, Rediet Abebe
Simplicial closure and higher-order link prediction. Proc. Natl. Acad. Sci. USA 115(48): E11221-E11230 (2018) - [c2]Michael T. Schaub
Flow Smoothing and Denoising: Graph Signal Processing in the Edge-Space. GlobalSIP 2018: 735-739 - [i18]Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, Jon M. Kleinberg:
Simplicial Closure and Higher-order Link Prediction. CoRR abs/1802.06916 (2018) - [i17]Michael T. Schaub, Jean-Charles Delvenne, Renaud Lambiotte, Mauricio Barahona:
Multiscale dynamical embeddings of complex networks. CoRR abs/1804.03733 (2018) - [i16]Michael T. Schaub, Jean-Charles Delvenne, Renaud Lambiotte, Mauricio Barahona
Structured networks and coarse-grained descriptions: a dynamical perspective. CoRR abs/1804.06268 (2018) - [i15]Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, Ali Jadbabaie:
Random Walks on Simplicial Complexes and the normalized Hodge Laplacian. CoRR abs/1807.05044 (2018) - [i14]Michael T. Schaub, Santiago Segarra:
Flow Smoothing and Denoising: Graph Signal Processing in the Edge-Space. CoRR abs/1808.02111 (2018) - 2017
- [j6]Michael T. Schaub
The many facets of community detection in complex networks. Appl. Netw. Sci. 2: 4 (2017) - [j5]Michael T. Schaub
Sparse Matrix Factorizations for Fast Linear Solvers with Application to Laplacian Systems. SIAM J. Matrix Anal. Appl. 38(2): 505-529 (2017) - [c1]Santiago Segarra
Network inference from consensus dynamics. CDC 2017: 3212-3217 - [i13]Marco Avella-Medina, Francesca Parise, Michael T. Schaub, Santiago Segarra:
Centrality measures for graphons. CoRR abs/1707.09350 (2017) - [i12]Santiago Segarra, Michael T. Schaub, Ali Jadbabaie:
Network Inference from Consensus Dynamics. CoRR abs/1708.05329 (2017) - [i11]Mauro Faccin, Michael T. Schaub, Jean-Charles Delvenne:
Entrograms and coarse graining of dynamics on complex networks. CoRR abs/1711.01987 (2017) - [i10]Martin Rosvall, Jean-Charles Delvenne, Michael T. Schaub, Renaud Lambiotte:
Different approaches to community detection. CoRR abs/1712.06468 (2017) - 2016
- [j4]Karol A. Bacik
Flow-Based Network Analysis of the Caenorhabditis elegans Connectome. PLoS Comput. Biol. 12(8) (2016) - [i9]Vsevolod Salnikov, Michael T. Schaub, Renaud Lambiotte:
Using higher-order Markov models to reveal flow-based communities in networks. CoRR abs/1601.03516 (2016) - [i8]Michael T. Schaub, Maguy Trefois, Paul Van Dooren, Jean-Charles Delvenne:
Sparse matrix factorizations for fast linear solvers with application to Laplacian systems. CoRR abs/1605.09148 (2016) - [i7]Michael T. Schaub, Neave O'Clery, Yazan N. Billeh, Jean-Charles Delvenne, Renaud Lambiotte, Mauricio Barahona:
Graph partitions and cluster synchronization in networks of oscillators. CoRR abs/1608.04283 (2016) - [i6]Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte:
The many facets of community detection in complex networks. CoRR abs/1611.07769 (2016) - [i5]Ernesto Estrada, Jean-Charles Delvenne, Naomichi Hatano, José L. Mateos, Ralf Metzler, Alejandro P. Riascos, Michael T. Schaub:
Random Multi-Hopper Model. Super-Fast Random Walks on Graphs. CoRR abs/1612.08631 (2016) - 2015
- [j3]Michael T. Schaub
Emergence of Slow-Switching Assemblies in Structured Neuronal Networks. PLoS Comput. Biol. 11(7) (2015) - 2014
- [j2]Michael T. Schaub
Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution. Netw. Sci. 2(1): 66-89 (2014) - 2013
- [i4]Michael T. Schaub, Jörg Lehmann, Sophia N. Yaliraki, Mauricio Barahona
Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution. CoRR abs/1303.6241 (2013) - [i3]Jean-Charles Delvenne, Michael T. Schaub, Sophia N. Yaliraki, Mauricio Barahona
The stability of a graph partition: A dynamics-based framework for community detection. CoRR abs/1308.1605 (2013) - 2012
- [j1]Michael T. Schaub
The Ising decoder: reading out the activity of large neural ensembles. J. Comput. Neurosci. 32(1): 101-118 (2012) - 2011
- [i2]Michael T. Schaub, Jean-Charles Delvenne, Sophia N. Yaliraki, Mauricio Barahona:
Markov dynamics as a zooming lens for multiscale community detection: non clique-like communities and the field-of-view limit. CoRR abs/1109.5593 (2011) - [i1]Michael T. Schaub, Renaud Lambiotte, Mauricio Barahona:
Coding of Markov dynamics for multiscale community detection in complex networks. CoRR abs/1109.6642 (2011)
Coauthor Index
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