Markov dynamics as a zooming lens for multiscale community detection: non clique-like communities and the field-of-view limit - Michael T. Schaub; Jean-Charles Delvenne; Sophia N. Yaliraki; Mauricio Barahona (CoRR, 2011)

Coding of Markov dynamics for multiscale community detection in complex networks - Michael T. Schaub; Renaud Lambiotte; Mauricio Barahona (CoRR, 2011)

The Ising decoder: reading out the activity of large neural ensembles. - Michael T. Schaub; Simon R. Schultz (J. Comput. Neurosci., 2012)

The stability of a graph partition: A dynamics-based framework for community detection. - Jean-Charles Delvenne; Michael T. Schaub; Sophia N. Yaliraki; Mauricio Barahona (CoRR, 2013)

Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution. - Michael T. Schaub; Jörg Lehmann; Sophia N. Yaliraki; Mauricio Barahona (Netw. Sci., 2014)

Emergence of Slow-Switching Assemblies in Structured Neuronal Networks. - Michael T. Schaub; Yazan N. Billeh; Costas A. Anastassiou; Christof Koch; Mauricio Barahona (PLoS Comput. Biol., 2015)

Using higher-order Markov models to reveal flow-based communities in networks. - Vsevolod Salnikov; Michael T. Schaub; Renaud Lambiotte (CoRR, 2016)

Graph partitions and cluster synchronization in networks of oscillators. - Michael T. Schaub; Neave O'Clery; Yazan N. Billeh; Jean-Charles Delvenne; Renaud Lambiotte; Mauricio Barahona (CoRR, 2016)

Flow-Based Network Analysis of the Caenorhabditis elegans Connectome. - Karol A. Bacik; Michael T. Schaub; Mariano Beguerisse-Díaz; Yazan N. Billeh; Mauricio Barahona (PLoS Comput. Biol., 2016)

Network inference from consensus dynamics. - Santiago Segarra; Michael T. Schaub; Ali Jadbabaie (CDC, 2017)

The many facets of community detection in complex networks. - Michael T. Schaub; Jean-Charles Delvenne; Martin Rosvall; Renaud Lambiotte (Appl. Netw. Sci., 2017)

Centrality measures for graphons. - Marco Avella-Medina; Francesca Parise; Michael T. Schaub; Santiago Segarra (CoRR, 2017)

Different approaches to community detection. - Martin Rosvall; Jean-Charles Delvenne; Michael T. Schaub; Renaud Lambiotte (CoRR, 2017)

Sparse Matrix Factorizations for Fast Linear Solvers with Application to Laplacian Systems. - Michael T. Schaub; Maguy Trefois; Paul Van Dooren; Jean-Charles Delvenne (SIAM J. Matrix Anal. Appl., 2017)

Flow Smoothing and Denoising: Graph Signal Processing in the Edge-Space. - Michael T. Schaub; Santiago Segarra (GlobalSIP, 2018)

Random multi-hopper model: super-fast random walks on graphs. - Ernesto Estrada; Jean-Charles Delvenne; Naomichi Hatano; José L. Mateos; Ralf Metzler; Alejandro P. Riascos; Michael T. Schaub (J. Complex Networks, 2018)

Entrograms and coarse graining of dynamics on complex networks. - Mauro Faccin; Michael T. Schaub; Jean-Charles Delvenne (J. Complex Networks, 2018)

Multiscale dynamical embeddings of complex networks. - Michael T. Schaub; Jean-Charles Delvenne; Renaud Lambiotte; Mauricio Barahona (CoRR, 2018)

Structured networks and coarse-grained descriptions: a dynamical perspective. - Michael T. Schaub; Jean-Charles Delvenne; Renaud Lambiotte; Mauricio Barahona (CoRR, 2018)

Random Walks on Simplicial Complexes and the normalized Hodge Laplacian. - Michael T. Schaub; Austin R. Benson; Paul Horn; Gabor Lippner; Ali Jadbabaie (CoRR, 2018)

Feedforward architectures driven by inhibitory interactions. - Yazan N. Billeh; Michael T. Schaub (J. Comput. Neurosci., 2018)

Simplicial closure and higher-order link prediction. - Austin R. Benson; Rediet Abebe; Michael T. Schaub; Ali Jadbabaie; Jon M. Kleinberg (Proc. Natl. Acad. Sci. USA, 2018)

Spectral Partitioning of Time-varying Networks with Unobserved Edges. - Michael T. Schaub; Santiago Segarra; Hoi-To Wai (ICASSP, 2019)

Graph-based Semi-Supervised & Active Learning for Edge Flows. - Junteng Jia; Michael T. Schaub; Santiago Segarra; Austin R. Benson (KDD, 2019)

Opinion Dynamics with Multi-body Interactions. - Leonie Neuhäuser; Michael T. Schaub; Andrew Mellor; Renaud Lambiotte (NetGCooP, 2020)

Hierarchical community structure in networks. - Michael T. Schaub; Leto Peel (CoRR, 2020)

Detectability of hierarchical communities in networks. - Leto Peel; Michael T. Schaub (CoRR, 2020)

Systematic edge uncertainty in attributed social networks and its effects on rankings of minority nodes. - Felix I. Stamm; Leonie Neuhäuser; Florian Lemmerich; Michael T. Schaub; Markus Strohmaier (CoRR, 2020)

Random Walks on Simplicial Complexes and the Normalized Hodge 1-Laplacian. - Michael T. Schaub; Austin R. Benson; Paul Horn; Gabor Lippner; Ali Jadbabaie (SIAM Rev., 2020)

Blind Identification of Stochastic Block Models from Dynamical Observations. - Michael T. Schaub; Santiago Segarra; John N. Tsitsiklis (SIAM J. Math. Data Sci., 2020)

Centrality Measures for Graphons: Accounting for Uncertainty in Networks. - Marco Avella-Medina; Francesca Parise; Michael T. Schaub; Santiago Segarra (IEEE Trans. Netw. Sci. Eng., 2020)

Network Inference From Consensus Dynamics With Unknown Parameters. - Yu Zhu 003; Michael T. Schaub; Ali Jadbabaie; Santiago Segarra (IEEE Trans. Signal Inf. Process. over Networks, 2020)

Exact Blind Community Detection From Signals on Multiple Graphs. - T. Mitchell Roddenberry; Michael T. Schaub; Hoi-To Wai; Santiago Segarra (IEEE Trans. Signal Process., 2020)

Outlier Detection for Trajectories via Flow-embeddings. - Florian Frantzen; Jean-Baptiste Seby; Michael T. Schaub (ACSCC, 2021)

Finite Impulse Response Filters for Simplicial Complexes. - Maosheng Yang; Elvin Isufi; Michael T. Schaub; Geert Leus (EUSIPCO, 2021)

Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes. - Leonie Neuhäuser; Felix I. Stamm; Florian Lemmerich; Michael T. Schaub; Markus Strohmaier (Appl. Netw. Sci., 2021)

Modularity maximisation for graphons. - Florian Klimm; Nick S. Jones; Michael T. Schaub (CoRR, 2021)

Consensus Dynamics and Opinion Formation on Hypergraphs. - Leonie Neuhäuser; Renaud Lambiotte; Michael T. Schaub (CoRR, 2021)

Local, global and scale-dependent node roles. - Michael Scholkemper; Michael T. Schaub (CoRR, 2021)

Signal processing on simplicial complexes. - Michael T. Schaub; Jean-Baptiste Seby; Florian Frantzen; T. Mitchell Roddenberry; Yu Zhu 003; Santiago Segarra (CoRR, 2021)

Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs. - Jiong Zhu; Junchen Jin; Michael T. Schaub; Danai Koutra (CoRR, 2021)

Consensus dynamics on temporal hypergraphs. - Leonie Neuhäuser; Renaud Lambiotte; Michael T. Schaub (CoRR, 2021)

Signal processing on higher-order networks: Livin' on the edge... and beyond. - Michael T. Schaub; Yu Zhu 003; Jean-Baptiste Seby; T. Mitchell Roddenberry; Santiago Segarra (Signal Process., 2021)

CrossTalkeR: analysis and visualization of ligand-receptorne tworks. - James Shiniti Nagai; Nils B. Leimkühler; Michael T. Schaub; Rebekka K. Schneider; Ivan G. Costa (Bioinform., 2021)

Higher-order signal processing with the Dirac operator. - Lucille Calmon; Michael T. Schaub; Ginestra Bianconi (IEEECONF, 2022)

Hodgelets: Localized Spectral Representations of Flows On Simplicial Complexes. - T. Mitchell Roddenberry; Florian Frantzen; Michael T. Schaub; Santiago Segarra (ICASSP, 2022)

Signal Processing On Cell Complexes. - T. Mitchell Roddenberry; Michael T. Schaub; Mustafa Hajij (ICASSP, 2022)

Blind Extraction of Equitable Partitions from Graph Signals. - Michael Scholkemper; Michael T. Schaub (ICASSP, 2022)

How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications. - Jiong Zhu; Junchen Jin; Donald Loveland; Michael T. Schaub; Danai Koutra (KDD, 2022)

On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. - Donald Loveland; Jiong Zhu; Mark Heimann; Benjamin Fish; Michael T. Schaub; Danai Koutra (CoRR, 2022)

Improving the visibility of minorities through network growth interventions. - Leonie Neuhäuser; Fariba Karimi 001; Jan Bachmann; Markus Strohmaier; Michael T. Schaub (CoRR, 2022)

Simplicial Convolutional Filters. - Maosheng Yang; Elvin Isufi; Michael T. Schaub; Geert Leus (IEEE Trans. Signal Process., 2022)

Modularity Maximization for Graphons. - Florian Klimm; Nick S. Jones; Michael T. Schaub (SIAM J. Appl. Math., 2022)

Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs. - Mustafa Hajij; Ghada Zamzmi; Theodore Papamarkou; AIdo Guzman-Saenz; Tolga Birdal; Michael T. Schaub (ACSSC, 2023)

Signal Processing On Product Spaces. - T. Mitchell Roddenberry; Vincent P. Grande; Florian Frantzen; Michael T. Schaub; Santiago Segarra (ICASSP, 2023)

Topological Point Cloud Clustering. - Vincent Peter Grande; Michael T. Schaub (ICML, 2023)

Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH. - Vincent Peter Grande; Michael T. Schaub (LoG, 2023)

Representing Edge Flows on Graphs via Sparse Cell Complexes. - Josef Hoppe; Michael T. Schaub (LoG, 2023)

On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. - Donald Loveland; Jiong Zhu; Mark Heimann; Benjamin Fish; Michael T. Schaub; Danai Koutra (LoG, 2023)

An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions. - Michael Scholkemper; Michael T. Schaub (NeurIPS, 2023)

ICML 2023 Topological Deep Learning Challenge: Design and Results. - Mathilde Papillon; Mustafa Hajij; Audun Myers; Florian Frantzen; Ghada Zamzmi; Helen Jenne; Johan Mathe; Josef Hoppe; Michael T. Schaub; Theodore Papamarkou; Aldo Guzmán-Sáenz; Bastian Rieck; et al. (TAG-ML, 2023)

Neighborhood Structure Configuration Models. - Felix I. Stamm; Michael Scholkemper; Michael T. Schaub; Markus Strohmaier (WWW, 2023)

Dirac signal processing of higher-order topological signals. - Lucille Calmon; Michael T. Schaub; Ginestra Bianconi (CoRR, 2023)

PyGenStability: Multiscale community detection with generalized Markov Stability. - Alexis Arnaudon; Dominik J. Schindler; Robert L. Peach; Adam Gosztolai; Maxwell Hodges; Michael T. Schaub; Mauricio Barahona (CoRR, 2023)

Learning the effective order of a hypergraph dynamical system. - Leonie Neuhäuser; Michael Scholkemper; Francesco Tudisco; Michael T. Schaub (CoRR, 2023)

ICML 2023 Topological Deep Learning Challenge : Design and Results. - 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; et al. (CoRR, 2023)

Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs. - Mustafa Hajij; Ghada Zamzmi; Theodore Papamarkou; Aldo Guzmán-Sáenz; Tolga Birdal; Michael T. Schaub (CoRR, 2023)

What Are Higher-Order Networks? - Christian Bick; Elizabeth Gross; Heather A. Harrington; Michael T. Schaub (SIAM Rev., 2023)

Topological Trajectory Classification and Landmark Inference on Simplicial Complexes. - Vincent P. Grande; Josef Hoppe; Florian Frantzen; Michael T. Schaub (IEEECONF, 2024)

Disentangling the Spectral Properties of the Hodge Laplacian: not all small Eigenvalues are Equal. - Vincent P. Grande; Michael T. Schaub (ICASSP, 2024)

Optimal Transport Distances for Directed, Weighted Graphs: A Case Study With Cell-Cell Communication Networks. - James Shiniti Nagai; Ivan G. Costa; Michael T. Schaub (ICASSP, 2024)

A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings. - Michael Scholkemper; Damin Kühn; Gerion Nabbefeld; Simon Musall; Björn Kampa; Michael T. Schaub (ICASSP, 2024)

Learning From Simplicial Data Based on Random Walks and 1D Convolutions. - Florian Frantzen; Michael T. Schaub (ICLR, 2024)

Position: Topological Deep Learning is the New Frontier for Relational Learning. - 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; et al. (ICML, 2024)

Graph Neural Networks Do Not Always Oversmooth. - Bastian Epping; Alexandre René; Moritz Helias; Michael T. Schaub (NeurIPS, 2024)

Faster optimal univariate microgaggregation. - Felix I. Stamm; Michael T. Schaub (CoRR, 2024)

TopoX: A Suite of Python Packages for Machine Learning on Topological Domains. - Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Rubén Ballester; Claudio Battiloro; Guillermo Bernárdez; Tolga Birdal; Aiden Brent; Peter Chin 001; Sergio Escalera; et al. (CoRR, 2024)

Position Paper: Challenges and Opportunities in Topological Deep Learning. - 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; et al. (CoRR, 2024)

Random Abstract Cell Complexes. - Josef Hoppe; Michael T. Schaub (CoRR, 2024)

Node-Level Topological Representation Learning on Point Clouds. - Vincent P. Grande; Michael T. Schaub (CoRR, 2024)

TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning. - Lev Telyatnikov; Guillermo Bernárdez; Marco Montagna; Pavlo Vasylenko; Ghada Zamzmi; Mustafa Hajij; Michael T. Schaub; Nina Miolane; Simone Scardapane; Theodore Papamarkou (CoRR, 2024)

Improving the Noise Estimation of Latent Neural Stochastic Differential Equations. - Linus Heck; Maximilian Gelbrecht; Michael T. Schaub; Niklas Boers (CoRR, 2024)

Faster optimal univariate microaggregation. - Felix I. Stamm; Michael T. Schaub (Trans. Mach. Learn. Res., 2024)

Algorithm 1044: PyGenStability, a Multiscale Community Detection Framework with Generalized Markov Stability. - Alexis Arnaudon; Dominik J. Schindler; Robert L. Peach; Adam Gosztolai; Maxwell Hodges; Michael T. Schaub; Mauricio Barahona (ACM Trans. Math. Softw., 2024)

Global Ground Metric Learning with Applications to scRNA data. - Damin Kühn; Michael T. Schaub (AISTATS, 2025)

A Bayesian Perspective on Uncertainty Quantification for Estimated Graph Signals. - Lennard Rompelberg; Michael T. Schaub (ICASSP, 2025)

Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs. - Michael Scholkemper; Xinyi Wu 003; Ali Jadbabaie; Michael T. Schaub (ICLR, 2025)

Efficient Sampling of Temporal Networks with Preserved Causality Structure. - Felix I. Stamm; Mehdi Naima; Michael T. Schaub (SDM, 2025)

Ellipsoidal Embeddings of Graphs. - Michaël Fanuel; Antoine Aspeel; Michael T. Schaub; Jean-Charles Delvenne (SIAM J. Appl. Math., 2025)

Convergence of gradient based training for linear Graph Neural Networks. - Dhiraj Patel; Anton Savostianov; Michael T. Schaub (CoRR, 2025)

Efficient Sparsification of Simplicial Complexes via Local Densities of States. - Anton Savostianov; Michael T. Schaub; Nicola Guglielmi; Francesco Tudisco (CoRR, 2025)

HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection. - Florian Frantzen; Michael T. Schaub (CoRR, 2025)

Don't be Afraid of Cell Complexes! An Introduction from an Applied Perspective. - Josef Hoppe; Vincent P. Grande; Michael T. Schaub (CoRR, 2025)