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ORCIDMichael Scholkemper
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2020 – today
- 2024
[c5]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: 9751-9755- [i8]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 abs/2401.03913 (2024) - [i7]Michael Scholkemper, Xinyi Wu, Ali Jadbabaie, Michael T. Schaub:
Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs. CoRR abs/2406.02997 (2024) - 2023
- [c4]Michael Scholkemper, Michael T. Schaub:
An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions. NeurIPS 2023 - [c3]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: 3-8 - [c2]Felix I. Stamm, Michael Scholkemper, Michael T. Schaub, Markus Strohmaier:
Neighborhood Structure Configuration Models. WWW 2023: 210-220 - [i6]Michael Scholkemper, Michael T. Schaub:
An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions. CoRR abs/2305.19087 (2023) - [i5]Leonie Neuhäuser, Michael Scholkemper, Francesco Tudisco, Michael T. Schaub:
Learning the effective order of a hypergraph dynamical system. CoRR abs/2306.01813 (2023) - [i4]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.:
ICML 2023 Topological Deep Learning Challenge : Design and Results. CoRR abs/2309.15188 (2023) - 2022
- [c1]Michael Scholkemper, Michael T. Schaub:
Blind Extraction of Equitable Partitions from Graph Signals. ICASSP 2022: 5832-5836 - [i3]Michael Scholkemper, Michael T. Schaub:
Blind Extraction of Equitable Partitions from Graph Signals. CoRR abs/2203.05407 (2022) - [i2]Felix I. Stamm, Michael Scholkemper, Markus Strohmaier, Michael T. Schaub:
Neighborhood Structure Configuration Models. CoRR abs/2210.06843 (2022) - 2021
- [i1]Michael Scholkemper, Michael T. Schaub:
Local, global and scale-dependent node roles. CoRR abs/2105.12598 (2021)
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