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

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

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

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

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

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

Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs. - Michael Scholkemper; Xinyi Wu 003; Ali Jadbabaie; Michael T. Schaub (ICLR, 2025)