Graph type expressivity and transformations. - Josephine M. Thomas; Silvia Beddar-Wiesing; Alice Moallemy-Oureh; Rüdiger Nather (CoRR, 2021)

Continuous-time generative graph neural network for attributed dynamic graphs: student research abstract. - Alice Moallemy-Oureh (SAC, 2022)

FDGNN: Fully Dynamic Graph Neural Network. - Alice Moallemy-Oureh; Silvia Beddar-Wiesing; Rüdiger Nather; Josephine M. Thomas (CoRR, 2022)

Graph Neural Networks Designed for Different Graph Types: A Survey. - Josephine Maria Thomas; Alice Moallemy-Oureh; Silvia Beddar-Wiesing; Clara Holzhüter (Trans. Mach. Learn. Res., 2023)

Weisfeiler-Lehman goes dynamic: An analysis of the expressive power of Graph Neural Networks for attributed and dynamic graphs. - Silvia Beddar-Wiesing; Giuseppe Alessio D'Inverno; Caterina Graziani; Veronica Lachi; Alice Moallemy-Oureh; Franco Scarselli; Josephine Maria Thomas (Neural Networks, 2024)

Expressive Pooling for Graph Neural Networks. - Veronica Lachi; Alice Moallemy-Oureh; Andreas Roth; Pascal Welke (Trans. Mach. Learn. Res., 2025)

Absolute Evaluation Measures for Machine Learning: A Survey. - Silvia Beddar-Wiesing; Alice Moallemy-Oureh; Marie Kempkes; Josephine M. Thomas (CoRR, 2025)