Publikationen

2024[ to top ]
  • Weisfeiler–Lehman goes dynamic: An analysis of the expressive power of Graph Neural Networks for attributed and dynamic graphs. Beddar-Wiesing, Silvia; D’Inverno, Alessio; Graziani, Caterina; Lachi, Veronica; Moallemy-Oureh, Alice; Scarselli, Franco; Thomas, Josephine. In Neural Networks, 173, bl 106213. Elsevier, 2024.
2023[ to top ]
  • Power flow forecasts at transmission grid nodes using Graph Neural Networks. Beinert, Dominik; Holzhüter, Clara; Thomas, Josephine; Vogt, Stephan. In Energy and AI, 14(1), bl 100262. Elsevier, 2023.
  • Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network. Moallemy-Oureh, Alice; Beddar-Wiesing, Silvia; Nather, Rüdiger; Thomas, Josephine. In Workshop on Temporal Graph Learning (TGL), NeurIPS, bll 1–7. 2023.
  • Graph Neural Networks Designed for Different Graph Types: A Survey. Thomas, Josephine; Moallemy-Oureh, Alice; Beddar-Wiesing, Silvia; Holzhüter, Clara. In Transactions on Machine Learning Research. 2023.
2022[ to top ]
  • Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs. Beddar-Wiesing, Silvia; D’Inverno, Giuseppe Alessio; Graziani, Caterina; Lachi, Veronica; Moallemy-Oureh, Alice; Scarselli, Franco; Thomas, Josephine. In arXiv e-prints, bl arXiv:2210.03990. 2022.
  • Graph Neural Networks Designed for Different Graph Types: A Survey. Thomas, Josephine M.; Moallemy-Oureh, Alice; Beddar-Wiesing, Silvia; Holzhüter, Clara. In arXiv e-prints, bl arXiv:2204.03080. 2022.
  • FDGNN: Fully Dynamic Graph Neural Network. Moallemy-Oureh, Alice; Beddar-Wiesing, Silcia; Nather, Rüdiger; Thomas, Josephine M. In arXiv e-prints, bl arXiv:2206.03469. 2022.
2021[ to top ]
  • A Note on the Modeling Power of Different Graph Types. Thomas, Josephine M.; Beddar-Wiesing, Silvia; Moallemy-Oureh, Alice; Nather, Rüdiger. In arXiv e-prints, bl arXiv:2109.10708. 2021.
2017[ to top ]
  • Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory. Durán, Claudio; Daminelli, Simone; M., Thomas Josephine; Haupt, V. Joachim; Schroeder, Michael; Cannistraci, Carlo Vittorio. In Briefings in Bioinformatics, 19(6), bll 1183–1202. 2017.
  • Machine learning meets complex networks via coalescent embedding in the hyperbolic space. Muscoloni, Alessandro; Thomas, Josephine Maria; Ciucci, Sara; Bianconi, Ginestra; Cannistraci, Carlo Vittorio. In Nature Communications, 8(1), bl 1615. Springer, 2017.
2015[ to top ]
  • Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks. Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Cannistraci, Carlo Vittorio. In New Journal of Physics, 17(11), bl 113037. IOP Publishing, 2015.