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)

TopoX: A Suite of Python Packages for Machine Learning on Topological Domains. - Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Rubén Ballester; Claudio Battiloro; Guillermo Bernárdez; Tolga Birdal; Aiden Brent; Peter Chin 001; Sergio Escalera; et al. (CoRR, 2024)

Nonlinear Sheaf Diffusion in Graph Neural Networks. - Olga Zaghen (CoRR, 2024)

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain. - Guillermo Bernárdez; Lev Telyatnikov; Marco Montagna; Federica Baccini; Mathilde Papillon; Miquel Ferriol Galmés; Mustafa Hajij; Theodore Papamarkou; Maria Sofia Bucarelli; Olga Zaghen; Johan Mathe; Audun Myers; et al. (CoRR, 2024)

Revisiting Random Walks for Learning on Graphs. - Jinwoo Kim; Olga Zaghen; Ayhan Suleymanzade; Youngmin Ryou; Seunghoon Hong (ICLR, 2025)

Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design. - Lev Telyatnikov; Maria Sofia Bucarelli; Guillermo Bernárdez; Olga Zaghen; Simone Scardapane; Pietro Lio (Trans. Mach. Learn. Res., 2025)

Towards Variational Flow Matching on General Geometries. - Olga Zaghen; Floor Eijkelboom; Alison Pouplin; Erik J. Bekkers (CoRR, 2025)