Michael T. Schaub

ORCID

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 106

Top Venues

Publications

Josef Hoppe, Michael T. Schaub
Random Abstract Cell Complexes. CoRR (2024)
Felix I. Stamm, Michael T. Schaub
Faster optimal univariate microgaggregation. CoRR (2024)
Michael Scholkemper, Damin Kühn, Gerion Nabbefeld, Simon Musall, Björn Kampa, Michael T. Schaub
A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings. ICASSP (2024)
Vincent P. Grande, Michael T. Schaub
Disentangling the Spectral Properties of the Hodge Laplacian: not all small Eigenvalues are Equal. ICASSP (2024)
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)
Florian Frantzen, Michael T. Schaub
Learning From Simplicial Data Based on Random Walks and 1D Convolutions. ICLR (2024)
Theodore Papamarkou, Tolga Birdal, Michael Bronstein, Gunnar 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
Position Paper: Challenges and Opportunities in Topological Deep Learning. CoRR (2024)
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 (2024)
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 (2024)
Michael Scholkemper, Xinyi Wu, Ali Jadbabaie, Michael T. Schaub
Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs. CoRR (2024)
Vincent P. Grande, Michael T. Schaub
Node-Level Topological Representation Learning on Point Clouds. CoRR (2024)
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
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains. CoRR (2024)
Bastian Epping, Alexandre René, Moritz Helias, Michael T. Schaub
Graph Neural Networks Do Not Always Oversmooth. CoRR (2024)
Michaël Fanuel, Antoine Aspeel, Michael T. Schaub, Jean-Charles Delvenne
Ellipsoidal embeddings of graphs. CoRR (2024)
Florian Frantzen, Michael T. Schaub
Learning From Simplicial Data Based on Random Walks and 1D Convolutions. CoRR (2024)
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 (2023)
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)
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 (2023)
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 (2023)
Christian Bick, Elizabeth Gross, Heather A. Harrington, Michael T. Schaub
What Are Higher-Order Networks? SIAM Rev. 65 (3) (2023)
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
ICML 2023 Topological Deep Learning Challenge: Design and Results. TAG-ML (2023)
Vincent P. Grande, Michael T. Schaub
Non-isotropic Persistent Homology: Leveraging the Metric Dependency of PH. CoRR (2023)
Lucille Calmon, Michael T. Schaub, Ginestra Bianconi
Dirac signal processing of higher-order topological signals. CoRR (2023)
Michael Scholkemper, Michael T. Schaub
An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions. NeurIPS (2023)
Michael Scholkemper, Michael T. Schaub
An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions. CoRR (2023)
Leonie Neuhäuser, Michael Scholkemper, Francesco Tudisco, Michael T. Schaub
Learning the effective order of a hypergraph dynamical system. CoRR (2023)
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 Meisner, 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.
ICML 2023 Topological Deep Learning Challenge : Design and Results. CoRR (2023)
T. Mitchell Roddenberry, Vincent P. Grande, Florian Frantzen, Michael T. Schaub, Santiago Segarra
Signal Processing On Product Spaces. ICASSP (2023)
Vincent Peter Grande, Michael T. Schaub
Topological Point Cloud Clustering. ICML (2023)
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)
Vincent Peter Grande, Michael T. Schaub
Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH. LoG (2023)
Felix I. Stamm, Michael Scholkemper, Michael T. Schaub, Markus Strohmaier
Neighborhood Structure Configuration Models. WWW (2023)
Vincent P. Grande, Michael T. Schaub
Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal. CoRR (2023)
Josef Hoppe, Michael T. Schaub
Representing Edge Flows on Graphs via Sparse Cell Complexes. LoG (2023)
Vincent P. Grande, Michael T. Schaub
Topological Point Cloud Clustering. CoRR (2023)
Josef Hoppe, Michael T. Schaub
Representing Edge Flows on Graphs via Sparse Cell Complexes. CoRR (2023)
Alexis Arnaudon, Dominik J. Schindler, Robert L. Peach, Adam Gosztolai, Maxwell Hodges, Michael T. Schaub, Mauricio Barahona
PyGenStability: Multiscale community detection with generalized Markov Stability. CoRR (2023)
Lucille Calmon, Michael T. Schaub, Ginestra Bianconi
Higher-order signal processing with the Dirac operator. CoRR (2022)
Maosheng Yang, Elvin Isufi, Michael T. Schaub, Geert Leus
Simplicial Convolutional Filters. IEEE Trans. Signal Process. 70 (2022)
Michael Scholkemper, Michael T. Schaub
Blind Extraction of Equitable Partitions from Graph Signals. ICASSP (2022)
T. Mitchell Roddenberry, Florian Frantzen, Michael T. Schaub, Santiago Segarra
Hodgelets: Localized Spectral Representations of Flows On Simplicial Complexes. ICASSP (2022)
Maosheng Yang, Elvin Isufi, Michael T. Schaub, Geert Leus
Simplicial Convolutional Filters. CoRR (2022)
Donald Loveland, Jiong Zhu, Mark Heimann, Ben Fish, Michael T. Schaub, Danai Koutra
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. CoRR (2022)
Lucille Calmon, Michael T. Schaub, Ginestra Bianconi
Higher-order signal processing with the Dirac operator. IEEECONF (2022)
Florian Klimm, Nick S. Jones, Michael T. Schaub
Modularity Maximization for Graphons. SIAM J. Appl. Math. 82 (6) (2022)
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)
Michael Scholkemper, Michael T. Schaub
Blind Extraction of Equitable Partitions from Graph Signals. CoRR (2022)
T. Mitchell Roddenberry, Michael T. Schaub, Mustafa Hajij
Signal Processing On Cell Complexes. ICASSP (2022)
Felix I. Stamm, Michael Scholkemper, Markus Strohmaier, Michael T. Schaub
Neighborhood Structure Configuration Models. CoRR (2022)
Leonie Neuhäuser, Fariba Karimi, Jan Bachmann, Markus Strohmaier, Michael T. Schaub
Improving the visibility of minorities through network growth interventions. CoRR (2022)
T. Mitchell Roddenberry, Florian Frantzen, Michael T. Schaub, Santiago Segarra
Hodgelets: Localized Spectral Representations of Flows on Simplicial Complexes. CoRR (2021)
Maosheng Yang, Elvin Isufi, Michael T. Schaub, Geert Leus
Finite Impulse Response Filters for Simplicial Complexes. EUSIPCO (2021)
Jiong Zhu, Junchen Jin, Michael T. Schaub, Danai Koutra
Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs. CoRR (2021)
Leonie Neuhäuser, Renaud Lambiotte, Michael T. Schaub
Consensus dynamics on temporal hypergraphs. CoRR (2021)
Leonie Neuhäuser, Felix I. Stamm, Florian Lemmerich, Michael T. Schaub, Markus Strohmaier
Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes. Appl. Netw. Sci. 6 (1) (2021)
Maosheng Yang, Elvin Isufi, Michael T. Schaub, Geert Leus
Finite Impulse Response Filters for Simplicial Complexes. CoRR (2021)
Florian Frantzen, Jean-Baptiste Seby, Michael T. Schaub
Outlier Detection for Trajectories via Flow-embeddings. CoRR (2021)
Michael T. Schaub, Jean-Baptiste Seby, Florian Frantzen, T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra
Signal processing on simplicial complexes. CoRR (2021)
Christian Bick, Elizabeth Gross, Heather A. Harrington, Michael T. Schaub
What are higher-order networks? CoRR (2021)
T. Mitchell Roddenberry, Michael T. Schaub, Mustafa Hajij
Signal Processing on Cell Complexes. CoRR (2021)
James S. Nagai, Nils B. Leimkühler, Michael T. Schaub, Rebekka K. Schneider, Ivan G. Costa
CrossTalkeR: analysis and visualization of ligand-receptorne tworks. Bioinform. 37 (22) (2021)
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. Signal Process. 187 (2021)
Leonie Neuhäuser, Renaud Lambiotte, Michael T. Schaub
Consensus Dynamics and Opinion Formation on Hypergraphs. CoRR (2021)
Florian Frantzen, Jean-Baptiste Seby, Michael T. Schaub
Outlier Detection for Trajectories via Flow-embeddings. ACSCC (2021)
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 (2021)
Florian Klimm, Nick S. Jones, Michael T. Schaub
Modularity maximisation for graphons. CoRR (2021)
Michael Scholkemper, Michael T. Schaub
Local, global and scale-dependent node roles. CoRR (2021)
Michael T. Schaub, Santiago Segarra, John N. Tsitsiklis
Blind Identification of Stochastic Block Models from Dynamical Observations. SIAM J. Math. Data Sci. 2 (2) (2020)
Felix I. Stamm, Leonie Neuhäuser, Florian Lemmerich, Michael T. Schaub, Markus Strohmaier
Systematic edge uncertainty in attributed social networks and its effects on rankings of minority nodes. CoRR (2020)
T. Mitchell Roddenberry, Michael T. Schaub, Hoi-To Wai, Santiago Segarra
Exact Blind Community Detection from Signals on Multiple Graphs. CoRR (2020)
Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, Ali Jadbabaie
Random Walks on Simplicial Complexes and the Normalized Hodge 1-Laplacian. SIAM Rev. 62 (2) (2020)
Marco Avella-Medina, Francesca Parise, Michael T. Schaub, Santiago Segarra
Centrality Measures for Graphons: Accounting for Uncertainty in Networks. IEEE Trans. Netw. Sci. Eng. 7 (1) (2020)
Leto Peel, Michael T. Schaub
Detectability of hierarchical communities in networks. CoRR (2020)
Yu Zhu, Michael T. Schaub, Ali Jadbabaie, Santiago Segarra
Network Inference From Consensus Dynamics With Unknown Parameters. IEEE Trans. Signal Inf. Process. over Networks 6 (2020)
Michael T. Schaub, Leto Peel
Hierarchical community structure in networks. CoRR (2020)
Leonie Neuhäuser, Michael T. Schaub, Andrew Mellor, Renaud Lambiotte
Opinion Dynamics with Multi-body Interactions. NetGCooP (2020)
T. Mitchell Roddenberry, Michael T. Schaub, Hoi-To Wai, Santiago Segarra
Exact Blind Community Detection From Signals on Multiple Graphs. IEEE Trans. Signal Process. 68 (2020)
Michael T. Schaub, Santiago Segarra, Hoi-To Wai
Spectral partitioning of time-varying networks with unobserved edges. CoRR (2019)
Michael T. Schaub, Santiago Segarra, John N. Tsitsiklis
Blind identification of stochastic block models from dynamical observations. CoRR (2019)
Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson
Graph-based Semi-Supervised & Active Learning for Edge Flows. KDD (2019)
Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson
Graph-based Semi-Supervised & Active Learning for Edge Flows. CoRR (2019)
Michael T. Schaub, Santiago Segarra, Hoi-To Wai
Spectral Partitioning of Time-varying Networks with Unobserved Edges. ICASSP (2019)
Yu Zhu, Michael T. Schaub, Ali Jadbabaie, Santiago Segarra
Network Inference from Consensus Dynamics with Unknown Parameters. CoRR (2019)
Michael T. Schaub, Jean-Charles Delvenne, Renaud Lambiotte, Mauricio Barahona
Multiscale dynamical embeddings of complex networks. CoRR (2018)
Mauro Faccin, Michael T. Schaub, Jean-Charles Delvenne
Entrograms and coarse graining of dynamics on complex networks. J. Complex Networks 6 (5) (2018)
Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, Jon M. Kleinberg
Simplicial Closure and Higher-order Link Prediction. CoRR (2018)
Michael T. Schaub, Santiago Segarra
Flow Smoothing and Denoising: Graph Signal Processing in the Edge-Space. CoRR (2018)
Michael T. Schaub, Santiago Segarra
Flow Smoothing and Denoising: Graph Signal Processing in the Edge-Space. GlobalSIP (2018)
Michael T. Schaub, Austin R. Benson, Paul Horn, Gabor Lippner, Ali Jadbabaie
Random Walks on Simplicial Complexes and the normalized Hodge Laplacian. CoRR (2018)
Yazan N. Billeh, Michael T. Schaub
Feedforward architectures driven by inhibitory interactions. J. Comput. Neurosci. 44 (1) (2018)
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. J. Complex Networks 6 (3) (2018)
Michael T. Schaub, Jean-Charles Delvenne, Renaud Lambiotte, Mauricio Barahona
Structured networks and coarse-grained descriptions: a dynamical perspective. CoRR (2018)
Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, Jon M. Kleinberg
Simplicial closure and higher-order link prediction. Proc. Natl. Acad. Sci. USA 115 (48) (2018)
Marco Avella-Medina, Francesca Parise, Michael T. Schaub, Santiago Segarra
Centrality measures for graphons. CoRR (2017)
Santiago Segarra, Michael T. Schaub, Ali Jadbabaie
Network inference from consensus dynamics. CDC (2017)
Santiago Segarra, Michael T. Schaub, Ali Jadbabaie
Network Inference from Consensus Dynamics. CoRR (2017)
Michael T. Schaub, Maguy Trefois, Paul Van Dooren, Jean-Charles Delvenne
Sparse Matrix Factorizations for Fast Linear Solvers with Application to Laplacian Systems. SIAM J. Matrix Anal. Appl. 38 (2) (2017)
Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte
The many facets of community detection in complex networks. Appl. Netw. Sci. 2 (2017)
Martin Rosvall, Jean-Charles Delvenne, Michael T. Schaub, Renaud Lambiotte
Different approaches to community detection. CoRR (2017)
Mauro Faccin, Michael T. Schaub, Jean-Charles Delvenne
Entrograms and coarse graining of dynamics on complex networks. CoRR (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Michael T. Schaub