Michael Scholkemper

According to our database, Michael Scholkemper authored at least 11 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Causal Cellular Context Transfer Learning (C3TL): An Efficient Architecture for Prediction of Unseen Perturbation Effects.

Michael Scholkemper, Sach Mukherjee

CoRR, March, 2026

2025

On the structural analysis of nodes in networks.

Michael Scholkemper

PhD thesis, 2025

Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs.

Michael Scholkemper, Xinyi Wu, Ali Jadbabaie, Michael T. Schaub

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

Proceedings of the IEEE International Conference on Acoustics, 2024

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, 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.

CoRR, 2023

Learning the effective order of a hypergraph dynamical system.

Leonie Neuhäuser, Michael Scholkemper, Francesco Tudisco, Michael T. Schaub

CoRR, 2023

Neighborhood Structure Configuration Models.

Felix I. Stamm, Michael Scholkemper, Michael T. Schaub, Markus Strohmaier

Proceedings of the ACM Web Conference 2023, 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, 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

Proceedings of the Topological, 2023

An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions.

Michael Scholkemper, Michael T. Schaub

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Blind Extraction of Equitable Partitions from Graph Signals.

Michael Scholkemper, Michael T. Schaub

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

Local, global and scale-dependent node roles.

Michael Scholkemper, Michael T. Schaub

CoRR, 2021