Michael T. Schaub

141 papers A* 10A 2Misc 10Journal 106Unranked 13
YearRankTypeTitle / Venue / Authors
2026 J jnl
Higher order trade-offs in hypergraph community detection.
CoRR
Jiaze Li, Michael T. Schaub, Leto Peel
2026 J jnl
Position: Message-passing and spectral GNNs are two sides of the same coin.
CoRR
Antonis Vasileiou, Juan Cerviño, Pascal Frossard, Charilaos I. Kanatsoulis, Christopher Morris, Michael T. Schaub, Pierre Vandergheynst, Zhiyang Wang, Guy Wolf, Ron Levie
2025 Misc conf
A Bayesian Perspective on Uncertainty Quantification for Estimated Graph Signals.
ICASSP
Lennard Rompelberg, Michael T. Schaub
2025 J jnl
Convergence of gradient based training for linear Graph Neural Networks.
CoRR
Dhiraj Patel, Anton Savostianov, Michael T. Schaub
2025 J jnl
Don't be Afraid of Cell Complexes! An Introduction from an Applied Perspective.
CoRR
Josef Hoppe, Vincent P. Grande, Michael T. Schaub
2025 A conf
Efficient Sampling of Temporal Networks with Preserved Causality Structure.
SDM
Felix I. Stamm, Mehdi Naima, Michael T. Schaub
2025 J jnl
Efficient Sampling of Temporal Networks with Preserved Causality Structure.
CoRR
Felix I. Stamm, Mehdi Naima, Michael T. Schaub
2025 J jnl
Efficient Sparsification of Simplicial Complexes via Local Densities of States.
CoRR
Anton Savostianov, Michael T. Schaub, Nicola Guglielmi, Francesco Tudisco
2025 J jnl
Ellipsoidal Embeddings of Graphs.
SIAM J. Appl. Math.
Michaël Fanuel, Antoine Aspeel, Michael T. Schaub, Jean-Charles Delvenne
2025 — conf
Faster Inference of Cell Complexes from Flows via Matrix Factorization.
EUSIPCO
Til Spreuer, Josef Hoppe, Michael T. Schaub
2025 J jnl
Faster Inference of Cell Complexes from Flows via Matrix Factorization.
CoRR
Til Spreuer, Josef Hoppe, Michael T. Schaub
2025 A conf
Global Ground Metric Learning with Applications to scRNA data.
AISTATS
Damin Kühn, Michael T. Schaub
2025 J jnl
Global Ground Metric Learning with Applications to scRNA data.
CoRR
Damin Kühn, Michael T. Schaub
2025 J jnl
Grassmanian Interpolation of Low-Pass Graph Filters: Theory and Applications.
CoRR
Anton Savostianov, Michael T. Schaub, Benjamin Stamm
2025 — conf
HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection.
KDD (2)
Florian Frantzen, Michael T. Schaub
2025 J jnl
HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection.
CoRR
Florian Frantzen, Michael T. Schaub
2025 A* conf
Point-Level Topological Representation Learning on Point Clouds.
ICML
Vincent Peter Grande, Michael T. Schaub
2025 A* conf
Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs.
ICLR
Michael Scholkemper, Xinyi Wu, Ali Jadbabaie, Michael T. Schaub
2025 J jnl
TopoBench: A Framework for Benchmarking Topological Deep Learning.
J. Data-centric Mach. Learn. Res.
Lev Telyatnikov, Guillermo Bernárdez, Marco Montagna, Mustafa Hajij, Martin Carrasco, Pavlo Vasylenko, Mathilde Papillon, Ghada Zamzmi, Michael T. Schaub, Jonas Verhellen, Pavel Snopov, Bertran Miquel-Oliver, Manel Gil-Sorribes, Alexis Molina, Victor Guallar, Theodore Long, Julian Suk, Patryk Rygiel, Alexander V. Nikitin, Giordan Escalona, Michael Banf, Dominik Filipiak, Liliya Imasheva, Max Schattauer, Alvaro L. Martinez, Halley Fritze, Marissa Masden, Valentina Sánchez, Manuel Lecha, Andrea Cavallo, Claudio Battiloro, Matthew Piekenbrock, Mauricio Tec, George Dasoulas, Nina Miolane, Simone Scardapane, Theodore Papamarkou
2025 J jnl
scACCorDiON: a clustering approach for explainable patient level cell-cell communication graph analysis.
Bioinform.
James Shiniti Nagai, Tiago Maié, Michael T. Schaub, Ivan G. Costa
2024 Misc conf
A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings.
ICASSP
Michael Scholkemper, Damin Kühn, Gerion Nabbefeld, Simon Musall, Björn Kampa, Michael T. Schaub
2024 J jnl
A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings.
CoRR
Michael Scholkemper, Damin Kühn, Gerion Nabbefeld, Simon Musall, Björn Kampa, Michael T. Schaub
2024 J jnl
Algorithm 1044: PyGenStability, a Multiscale Community Detection Framework with Generalized Markov Stability.
ACM Trans. Math. Softw.
Alexis Arnaudon, Dominik J. Schindler, Robert L. Peach, Adam Gosztolai, Maxwell Hodges, Michael T. Schaub, Mauricio Barahona
2024 Misc conf
Disentangling the Spectral Properties of the Hodge Laplacian: not all small Eigenvalues are Equal.
ICASSP
Vincent P. Grande, Michael T. Schaub
2024 J jnl
Ellipsoidal embeddings of graphs.
CoRR
Michaël Fanuel, Antoine Aspeel, Michael T. Schaub, Jean-Charles Delvenne
2024 J jnl
Faster optimal univariate microaggregation.
Trans. Mach. Learn. Res.
Felix I. Stamm, Michael T. Schaub
2024 J jnl
Faster optimal univariate microgaggregation.
CoRR
Felix I. Stamm, Michael T. Schaub
2024 A* conf
Graph Neural Networks Do Not Always Oversmooth.
NeurIPS
Bastian Epping, Alexandre René, Moritz Helias, Michael T. Schaub
2024 J jnl
Graph Neural Networks Do Not Always Oversmooth.
CoRR
Bastian Epping, Alexandre René, Moritz Helias, Michael T. Schaub
2024 J jnl
Improving the Noise Estimation of Latent Neural Stochastic Differential Equations.
CoRR
Linus Heck, Maximilian Gelbrecht, Michael T. Schaub, Niklas Boers
2024 A* conf
Learning From Simplicial Data Based on Random Walks and 1D Convolutions.
ICLR
Florian Frantzen, Michael T. Schaub
2024 J jnl
Learning From Simplicial Data Based on Random Walks and 1D Convolutions.
CoRR
Florian Frantzen, Michael T. Schaub
2024 J jnl
Node-Level Topological Representation Learning on Point Clouds.
CoRR
Vincent P. Grande, Michael T. Schaub
2024 Misc conf
Optimal Transport Distances for Directed, Weighted Graphs: A Case Study With Cell-Cell Communication Networks.
ICASSP
James Shiniti Nagai, Ivan G. Costa, Michael T. Schaub
2024 J jnl
Position Paper: Challenges and Opportunities in Topological Deep Learning.
CoRR
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, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi
2024 A* conf
Position: Topological Deep Learning is the New Frontier for Relational Learning.
ICML
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
2024 J jnl
Random Abstract Cell Complexes.
CoRR
Josef Hoppe, Michael T. Schaub
2024 J jnl
Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs.
CoRR
Michael Scholkemper, Xinyi Wu, Ali Jadbabaie, Michael T. Schaub
2024 J jnl
TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning.
CoRR
Lev Telyatnikov, Guillermo Bernárdez, Marco Montagna, Pavlo Vasylenko, Ghada Zamzmi, Mustafa Hajij, Michael T. Schaub, Nina Miolane, Simone Scardapane, Theodore Papamarkou
2024 J jnl
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains.
CoRR
Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Rubén Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane
2024 J jnl
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains.
J. Mach. Learn. Res.
Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Rubén Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane
2024 — conf
Topological Trajectory Classification and Landmark Inference on Simplicial Complexes.
IEEECONF
Vincent P. Grande, Josef Hoppe, Florian Frantzen, Michael T. Schaub
2024 J jnl
Topological Trajectory Classification and Landmark Inference on Simplicial Complexes.
CoRR
Vincent P. Grande, Josef Hoppe, Florian Frantzen, Michael T. Schaub
2023 A* conf
An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions.
NeurIPS
Michael Scholkemper, Michael T. Schaub
2023 J jnl
An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions.
CoRR
Michael Scholkemper, Michael T. Schaub
2023 Misc conf
Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs.
ACSSC
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, AIdo Guzman-Saenz, Tolga Birdal, Michael T. Schaub
2023 J jnl
Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs.
CoRR
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Aldo Guzmán-Sáenz, Tolga Birdal, Michael T. Schaub
2023 J jnl
Dirac signal processing of higher-order topological signals.
CoRR
Lucille Calmon, Michael T. Schaub, Ginestra Bianconi
2023 J jnl
Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal.
CoRR
Vincent P. Grande, Michael T. Schaub
2023 J jnl
ICML 2023 Topological Deep Learning Challenge : Design and Results.
CoRR
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, Michael Scholkemper, Robin Walters, Jens Agerberg, Georg Bökman, Sadrodin Barikbin, Claudio Battiloro, Gleb Bazhenov, Guillermo Bernárdez, Aiden Brent, Sergio Escalera, Simone Fiorellino, Dmitrii Gavrilev, Mohammed Hassanin, Paul Häusner, Odin Hoff Gardaa, Abdelwahed Khamis, Manuel Lecha, German Magai, Tatiana Malygina, Pavlo Melnyk, et al.
2023 — conf
ICML 2023 Topological Deep Learning Challenge: Design and Results.
TAG-ML
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, Neal Livesay, Tamal K. Dey, Abraham Rabinowitz, Aiden Brent, Alessandro Salatiello, Alexander Nikitin, Ali Zia, Claudio Battiloro, Dmitrii Gavrilev, Georg Bökman, German Magai, Gleb Bazhenov, Guillermo Bernárdez, Indro Spinelli, Jens Agerberg, Kalyan Varma Nadimpalli, Lev Telyatnikov, Luca Scofano, Lucia Testa, Manuel Lecha, Maosheng Yang, Mohammed Hassanin, Odin Hoff Gardaa, Olga Zaghen, Paul Häusner, Paul Snopoff, Pavlo Melnyk, Rubén Ballester, Sadrodin Barikbin, Sergio Escalera, Simone Fiorellino, Henry Kvinge, Jan Meissner, Karthikeyan Natesan Ramamurthy, Michael Scholkemper, Paul Rosen, Robin Walters, Shreyas N. Samaga, Soham Mukherjee, Sophia Sanborn, Tegan Emerson, Timothy Doster, Tolga Birdal, Vincent P. Grande, Abdelwahed Khamis, Simone Scardapane, Suraj Singh, Tatiana Malygina, Yixiao Yue, Nina Miolane
2023 J jnl
Learning the effective order of a hypergraph dynamical system.
CoRR
Leonie Neuhäuser, Michael Scholkemper, Francesco Tudisco, Michael T. Schaub
2023 A* conf
Neighborhood Structure Configuration Models.
WWW
Felix I. Stamm, Michael Scholkemper, Michael T. Schaub, Markus Strohmaier
2023 — conf
Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH.
LoG
Vincent Peter Grande, Michael T. Schaub
2023 J jnl
Non-isotropic Persistent Homology: Leveraging the Metric Dependency of PH.
CoRR
Vincent P. Grande, Michael T. Schaub
2023 — conf
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks.
LoG
Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
2023 J jnl
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks.
CoRR
Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
2023 J jnl
Optimal transport distances for directed, weighted graphs: a case study with cell-cell communication networks.
CoRR
James Shiniti Nagai, Ivan G. Costa, Michael T. Schaub
2023 J jnl
PyGenStability: Multiscale community detection with generalized Markov Stability.
CoRR
Alexis Arnaudon, Dominik J. Schindler, Robert L. Peach, Adam Gosztolai, Maxwell Hodges, Michael T. Schaub, Mauricio Barahona
2023 — conf
Representing Edge Flows on Graphs via Sparse Cell Complexes.
LoG
Josef Hoppe, Michael T. Schaub
2023 J jnl
Representing Edge Flows on Graphs via Sparse Cell Complexes.
CoRR
Josef Hoppe, Michael T. Schaub
2023 Misc conf
Signal Processing On Product Spaces.
ICASSP
T. Mitchell Roddenberry, Vincent P. Grande, Florian Frantzen, Michael T. Schaub, Santiago Segarra
2023 A* conf
Topological Point Cloud Clustering.
ICML
Vincent Peter Grande, Michael T. Schaub
2023 J jnl
Topological Point Cloud Clustering.
CoRR
Vincent P. Grande, Michael T. Schaub
2023 J jnl
What Are Higher-Order Networks?
SIAM Rev.
Christian Bick, Elizabeth Gross, Heather A. Harrington, Michael T. Schaub
2022 Misc conf
Blind Extraction of Equitable Partitions from Graph Signals.
ICASSP
Michael Scholkemper, Michael T. Schaub
2022 J jnl
Blind Extraction of Equitable Partitions from Graph Signals.
CoRR
Michael Scholkemper, Michael T. Schaub
2022 — conf
Higher-order signal processing with the Dirac operator.
IEEECONF
Lucille Calmon, Michael T. Schaub, Ginestra Bianconi
2022 J jnl
Higher-order signal processing with the Dirac operator.
CoRR
Lucille Calmon, Michael T. Schaub, Ginestra Bianconi
2022 Misc conf
Hodgelets: Localized Spectral Representations of Flows On Simplicial Complexes.
ICASSP
T. Mitchell Roddenberry, Florian Frantzen, Michael T. Schaub, Santiago Segarra
2022 A* conf
How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications.
KDD
Jiong Zhu, Junchen Jin, Donald Loveland, Michael T. Schaub, Danai Koutra
2022 J jnl
Improving the visibility of minorities through network growth interventions.
CoRR
Leonie Neuhäuser, Fariba Karimi, Jan Bachmann, Markus Strohmaier, Michael T. Schaub
2022 J jnl
Modularity Maximization for Graphons.
SIAM J. Appl. Math.
Florian Klimm, Nick S. Jones, Michael T. Schaub
2022 J jnl
Neighborhood Structure Configuration Models.
CoRR
Felix I. Stamm, Michael Scholkemper, Markus Strohmaier, Michael T. Schaub
2022 J jnl
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods.
CoRR
Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
2022 Misc conf
Signal Processing On Cell Complexes.
ICASSP
T. Mitchell Roddenberry, Michael T. Schaub, Mustafa Hajij
2022 J jnl
Simplicial Convolutional Filters.
CoRR
Maosheng Yang, Elvin Isufi, Michael T. Schaub, Geert Leus
2022 J jnl
Simplicial Convolutional Filters.
IEEE Trans. Signal Process.
Maosheng Yang, Elvin Isufi, Michael T. Schaub, Geert Leus
2021 J jnl
Consensus Dynamics and Opinion Formation on Hypergraphs.
CoRR
Leonie Neuhäuser, Renaud Lambiotte, Michael T. Schaub
2021 J jnl
Consensus dynamics on temporal hypergraphs.
CoRR
Leonie Neuhäuser, Renaud Lambiotte, Michael T. Schaub
2021 J jnl
CrossTalkeR: analysis and visualization of ligand-receptorne tworks.
Bioinform.
James Shiniti Nagai, Nils B. Leimkühler, Michael T. Schaub, Rebekka K. Schneider, Ivan G. Costa
2021 — conf
Finite Impulse Response Filters for Simplicial Complexes.
EUSIPCO
Maosheng Yang, Elvin Isufi, Michael T. Schaub, Geert Leus
2021 J jnl
Finite Impulse Response Filters for Simplicial Complexes.
CoRR
Maosheng Yang, Elvin Isufi, Michael T. Schaub, Geert Leus
2021 J jnl
Hodgelets: Localized Spectral Representations of Flows on Simplicial Complexes.
CoRR
T. Mitchell Roddenberry, Florian Frantzen, Michael T. Schaub, Santiago Segarra
2021 J jnl
Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs.
CoRR
Jiong Zhu, Junchen Jin, Michael T. Schaub, Danai Koutra
2021 J jnl
Local, global and scale-dependent node roles.
CoRR
Michael Scholkemper, Michael T. Schaub
2021 J jnl
Modularity maximisation for graphons.
CoRR
Florian Klimm, Nick S. Jones, Michael T. Schaub
2021 — conf
Outlier Detection for Trajectories via Flow-embeddings.
ACSCC
Florian Frantzen, Jean-Baptiste Seby, Michael T. Schaub
2021 J jnl
Outlier Detection for Trajectories via Flow-embeddings.
CoRR
Florian Frantzen, Jean-Baptiste Seby, Michael T. Schaub
2021 J jnl
Signal Processing on Cell Complexes.
CoRR
T. Mitchell Roddenberry, Michael T. Schaub, Mustafa Hajij
2021 J jnl
Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond.
CoRR
Michael T. Schaub, Yu Zhu, Jean-Baptiste Seby, T. Mitchell Roddenberry, Santiago Segarra
2021 J jnl
Signal processing on higher-order networks: Livin' on the edge... and beyond.
Signal Process.
Michael T. Schaub, Yu Zhu, Jean-Baptiste Seby, T. Mitchell Roddenberry, Santiago Segarra
2021 J jnl
Signal processing on simplicial complexes.
CoRR
Michael T. Schaub, Jean-Baptiste Seby, Florian Frantzen, T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra
2021 J jnl
Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes.
Appl. Netw. Sci.
Leonie Neuhäuser, Felix I. Stamm, Florian Lemmerich, Michael T. Schaub, Markus Strohmaier
2021 J jnl
What are higher-order networks?
CoRR
Christian Bick, Elizabeth Gross, Heather A. Harrington, Michael T. Schaub
2020 J jnl
Blind Identification of Stochastic Block Models from Dynamical Observations.
SIAM J. Math. Data Sci.
Michael T. Schaub, Santiago Segarra, John N. Tsitsiklis
2020 J jnl
Centrality Measures for Graphons: Accounting for Uncertainty in Networks.
IEEE Trans. Netw. Sci. Eng.
Marco Avella-Medina, Francesca Parise, Michael T. Schaub, Santiago Segarra
2020 J jnl
Detectability of hierarchical communities in networks.
CoRR
Leto Peel, Michael T. Schaub
2020 J jnl
Exact Blind Community Detection From Signals on Multiple Graphs.
IEEE Trans. Signal Process.
T. Mitchell Roddenberry, Michael T. Schaub, Hoi-To Wai, Santiago Segarra
2020 J jnl
Exact Blind Community Detection from Signals on Multiple Graphs.
CoRR
T. Mitchell Roddenberry, Michael T. Schaub, Hoi-To Wai, Santiago Segarra
2020 J jnl
Hierarchical community structure in networks.
CoRR
Michael T. Schaub, Leto Peel
2020 J jnl
Network Inference From Consensus Dynamics With Unknown Parameters.
IEEE Trans. Signal Inf. Process. over Networks
Yu Zhu, Michael T. Schaub, Ali Jadbabaie, Santiago Segarra
2020 — conf
Opinion Dynamics with Multi-body Interactions.
NetGCooP
Leonie Neuhäuser, Michael T. Schaub, Andrew Mellor, Renaud Lambiotte
2020 J jnl
Random Walks on Simplicial Complexes and the Normalized Hodge 1-Laplacian.
SIAM Rev.
Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, Ali Jadbabaie
2020 J jnl
Systematic edge uncertainty in attributed social networks and its effects on rankings of minority nodes.
CoRR
Felix I. Stamm, Leonie Neuhäuser, Florian Lemmerich, Michael T. Schaub, Markus Strohmaier
2019 J jnl
Blind identification of stochastic block models from dynamical observations.
CoRR
Michael T. Schaub, Santiago Segarra, John N. Tsitsiklis
2019 A* conf
Graph-based Semi-Supervised & Active Learning for Edge Flows.
KDD
Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson
2019 J jnl
Graph-based Semi-Supervised & Active Learning for Edge Flows.
CoRR
Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson
2019 J jnl
Network Inference from Consensus Dynamics with Unknown Parameters.
CoRR
Yu Zhu, Michael T. Schaub, Ali Jadbabaie, Santiago Segarra
2019 Misc conf
Spectral Partitioning of Time-varying Networks with Unobserved Edges.
ICASSP
Michael T. Schaub, Santiago Segarra, Hoi-To Wai
2019 J jnl
Spectral partitioning of time-varying networks with unobserved edges.
CoRR
Michael T. Schaub, Santiago Segarra, Hoi-To Wai
2018 J jnl
Entrograms and coarse graining of dynamics on complex networks.
J. Complex Networks
Mauro Faccin, Michael T. Schaub, Jean-Charles Delvenne
2018 J jnl
Feedforward architectures driven by inhibitory interactions.
J. Comput. Neurosci.
Yazan N. Billeh, Michael T. Schaub
2018 — conf
Flow Smoothing and Denoising: Graph Signal Processing in the Edge-Space.
GlobalSIP
Michael T. Schaub, Santiago Segarra
2018 J jnl
Flow Smoothing and Denoising: Graph Signal Processing in the Edge-Space.
CoRR
Michael T. Schaub, Santiago Segarra
2018 J jnl
Multiscale dynamical embeddings of complex networks.
CoRR
Michael T. Schaub, Jean-Charles Delvenne, Renaud Lambiotte, Mauricio Barahona
2018 J jnl
Random Walks on Simplicial Complexes and the normalized Hodge Laplacian.
CoRR
Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, Ali Jadbabaie
2018 J jnl
Random multi-hopper model: super-fast random walks on graphs.
J. Complex Networks
Ernesto Estrada, Jean-Charles Delvenne, Naomichi Hatano, José L. Mateos, Ralf Metzler, Alejandro P. Riascos, Michael T. Schaub
2018 J jnl
Simplicial Closure and Higher-order Link Prediction.
CoRR
Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, Jon M. Kleinberg
2018 J jnl
Simplicial closure and higher-order link prediction.
Proc. Natl. Acad. Sci. USA
Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, Jon M. Kleinberg
2018 J jnl
Structured networks and coarse-grained descriptions: a dynamical perspective.
CoRR
Michael T. Schaub, Jean-Charles Delvenne, Renaud Lambiotte, Mauricio Barahona
2017 J jnl
Centrality measures for graphons.
CoRR
Marco Avella-Medina, Francesca Parise, Michael T. Schaub, Santiago Segarra
2017 J jnl
Different approaches to community detection.
CoRR
Martin Rosvall, Jean-Charles Delvenne, Michael T. Schaub, Renaud Lambiotte
2017 J jnl
Entrograms and coarse graining of dynamics on complex networks.
CoRR
Mauro Faccin, Michael T. Schaub, Jean-Charles Delvenne
2017 J jnl
Network Inference from Consensus Dynamics.
CoRR
Santiago Segarra, Michael T. Schaub, Ali Jadbabaie
2017 — conf
Network inference from consensus dynamics.
CDC
Santiago Segarra, Michael T. Schaub, Ali Jadbabaie
2017 J jnl
Sparse Matrix Factorizations for Fast Linear Solvers with Application to Laplacian Systems.
SIAM J. Matrix Anal. Appl.
Michael T. Schaub, Maguy Trefois, Paul Van Dooren, Jean-Charles Delvenne
2017 J jnl
The many facets of community detection in complex networks.
Appl. Netw. Sci.
Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte
2016 J jnl
Flow-Based Network Analysis of the Caenorhabditis elegans Connectome.
PLoS Comput. Biol.
Karol A. Bacik, Michael T. Schaub, Mariano Beguerisse-Díaz, Yazan N. Billeh, Mauricio Barahona
2016 J jnl
Graph partitions and cluster synchronization in networks of oscillators.
CoRR
Michael T. Schaub, Neave O'Clery, Yazan N. Billeh, Jean-Charles Delvenne, Renaud Lambiotte, Mauricio Barahona
2016 J jnl
Random Multi-Hopper Model. Super-Fast Random Walks on Graphs.
CoRR
Ernesto Estrada, Jean-Charles Delvenne, Naomichi Hatano, José L. Mateos, Ralf Metzler, Alejandro P. Riascos, Michael T. Schaub
2016 J jnl
Sparse matrix factorizations for fast linear solvers with application to Laplacian systems.
CoRR
Michael T. Schaub, Maguy Trefois, Paul Van Dooren, Jean-Charles Delvenne
2016 J jnl
The many facets of community detection in complex networks.
CoRR
Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte
2016 J jnl
Using higher-order Markov models to reveal flow-based communities in networks.
CoRR
Vsevolod Salnikov, Michael T. Schaub, Renaud Lambiotte
2015 J jnl
Emergence of Slow-Switching Assemblies in Structured Neuronal Networks.
PLoS Comput. Biol.
Michael T. Schaub, Yazan N. Billeh, Costas A. Anastassiou, Christof Koch, Mauricio Barahona
2014 J jnl
Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution.
Netw. Sci.
Michael T. Schaub, Jörg Lehmann, Sophia N. Yaliraki, Mauricio Barahona
2013 J jnl
Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution
CoRR
Michael T. Schaub, Jörg Lehmann, Sophia N. Yaliraki, Mauricio Barahona
2013 J jnl
The stability of a graph partition: A dynamics-based framework for community detection.
CoRR
Jean-Charles Delvenne, Michael T. Schaub, Sophia N. Yaliraki, Mauricio Barahona
2012 J jnl
The Ising decoder: reading out the activity of large neural ensembles.
J. Comput. Neurosci.
Michael T. Schaub, Simon R. Schultz
2011 J jnl
Coding of Markov dynamics for multiscale community detection in complex networks
CoRR
Michael T. Schaub, Renaud Lambiotte, Mauricio Barahona
2011 J jnl
Markov dynamics as a zooming lens for multiscale community detection: non clique-like communities and the field-of-view limit
CoRR
Michael T. Schaub, Jean-Charles Delvenne, Sophia N. Yaliraki, Mauricio Barahona