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

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

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

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

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

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

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

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

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

Position: Topological Deep Learning is the New Frontier for Relational Learning. - Papamarkou, Theodore; Birdal, Tolga; Bronstein, Michael M.; Carlsson, Gunnar E.; Curry, Justin; Gao, Yue; Hajij, Mustafa; Kwitt, Roland; Lio, Pietro; Lorenzo, Paolo Di; Maroulas, Vasileios; Miolane, Nina; et al. (2024)

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

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

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

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

Position Paper: Challenges and Opportunities in Topological Deep Learning. - Papamarkou, Theodore; Birdal, Tolga; Bronstein, Michael M.; Carlsson, Gunnar E.; Curry, Justin; Gao, Yue; Hajij, Mustafa; Kwitt, Roland; Liò, Pietro; Lorenzo, Paolo Di; Maroulas, Vasileios; Miolane, Nina; et al. (CoRR, 2024)

Ellipsoidal embeddings of graphs. - Fanuel, Michaël; Aspeel, Antoine; Schaub, Michael T.; Delvenne, Jean-Charles (CoRR, 2024)

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

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

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

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

Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs. - Scholkemper, Michael; Wu, Xinyi; Jadbabaie, Ali; Schaub, Michael T. (CoRR, 2024)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Optimal transport distances for directed, weighted graphs: a case study with cell-cell communication networks. - Nagai, James Shiniti; Costa, Ivan G.; Schaub, Michael T. (CoRR, 2023)

ICML 2023 Topological Deep Learning Challenge : Design and Results. - Papillon, Mathilde; Hajij, Mustafa; Frantzen, Florian; Hoppe, Josef; Jenne, Helen; Mathe, Johan; Myers, Audun; Papamarkou, Theodore; Schaub, Michael T.; Zamzmi, Ghada; Birdal, Tolga; Dey, Tamal K.; et al. (CoRR, 2023)

Non-isotropic Persistent Homology: Leveraging the Metric Dependency of PH. - Grande, Vincent P.; Schaub, Michael T. (CoRR, 2023)

Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal. - Grande, Vincent P.; Schaub, Michael T. (CoRR, 2023)

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

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

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

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

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

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

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

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

Simplicial Convolutional Filters. - Yang, Maosheng; Isufi, Elvin; Schaub, Michael T.; Leus, Geert (CoRR, 2022)

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

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

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

Neighborhood Structure Configuration Models. - Stamm, Felix I.; Scholkemper, Michael; Strohmaier, Markus; Schaub, Michael T. (CoRR, 2022)

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

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

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

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

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

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

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

Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond. - Schaub, Michael T.; Zhu, Yu; Seby, Jean-Baptiste; Roddenberry, T. Mitchell; Segarra, Santiago (CoRR, 2021)

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

What are higher-order networks? - Bick, Christian; Gross, Elizabeth; Harrington, Heather A.; Schaub, Michael T. (CoRR, 2021)

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

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

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

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

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

Hodgelets: Localized Spectral Representations of Flows on Simplicial Complexes. - Roddenberry, T. Mitchell; Frantzen, Florian; Schaub, Michael T.; Segarra, Santiago (CoRR, 2021)

Signal Processing on Cell Complexes. - Roddenberry, T. Mitchell; Schaub, Michael T.; Hajij, Mustafa (CoRR, 2021)

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

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

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

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

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

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

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

Exact Blind Community Detection from Signals on Multiple Graphs. - Roddenberry, T. Mitchell; Schaub, Michael T.; Wai, Hoi-To; Segarra, Santiago (CoRR, 2020)

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

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

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

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

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

Spectral partitioning of time-varying networks with unobserved edges. - Schaub, Michael T.; Segarra, Santiago; Wai, Hoi-To (CoRR, 2019)

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

Blind identification of stochastic block models from dynamical observations. - Schaub, Michael T.; Segarra, Santiago; Tsitsiklis, John N. (CoRR, 2019)

Network Inference from Consensus Dynamics with Unknown Parameters. - Zhu, Yu; Schaub, Michael T.; Jadbabaie, Ali; Segarra, Santiago (CoRR, 2019)

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

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

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

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

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

Simplicial Closure and Higher-order Link Prediction. - Benson, Austin R.; Abebe, Rediet; Schaub, Michael T.; Jadbabaie, Ali; Kleinberg, Jon M. (CoRR, 2018)

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

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

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

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

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

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

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

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

Network Inference from Consensus Dynamics. - Segarra, Santiago; Schaub, Michael T.; Jadbabaie, Ali (CoRR, 2017)

Entrograms and coarse graining of dynamics on complex networks. - Faccin, Mauro; Schaub, Michael T.; Delvenne, Jean-Charles (CoRR, 2017)

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

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

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

Sparse matrix factorizations for fast linear solvers with application to Laplacian systems. - Schaub, Michael T.; Trefois, Maguy; Dooren, Paul Van; Delvenne, Jean-Charles (CoRR, 2016)

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

The many facets of community detection in complex networks. - Schaub, Michael T.; Delvenne, Jean-Charles; Rosvall, Martin; Lambiotte, Renaud (CoRR, 2016)

Random Multi-Hopper Model. Super-Fast Random Walks on Graphs. - Estrada, Ernesto; Delvenne, Jean-Charles; Hatano, Naomichi; Mateos, José L.; Metzler, Ralf; Riascos, Alejandro P.; Schaub, Michael T. (CoRR, 2016)

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

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

Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution - Schaub, Michael T.; Lehmann, Jörg; Yaliraki, Sophia N.; Barahona, Mauricio (CoRR, 2013)

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

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

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

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