2023[ to top ]
  • Beinert, D., Holzhüter, C., Thomas, J., Vogt, S.: Power flow forecasts at transmission grid nodes using Graph Neural Networks Energy and AI. 14, 100262 (2023).
  • Thomas, J., Moallemy-Oureh, A., Beddar-Wiesing, S., Holzhüter, C.: Graph Neural Networks Designed for Different Graph Types: A Survey Transactions on Machine Learning Research. (2023).
2022[ to top ]
  • Beddar-Wiesing, S., D’Inverno, G.A., Graziani, C., Lachi, V., Moallemy-Oureh, A., Scarselli, F., Thomas, J.: Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs arXiv e-prints. arXiv:2210.03990 (2022).
2021[ to top ]
  • Thomas, J.M., Beddar-Wiesing, S., Moallemy-Oureh, A., Nather, R.: A Note on the Modeling Power of Different Graph Types arXiv e-prints. arXiv:2109.10708 (2021).
2017[ to top ]
  • Muscoloni, A., Thomas, J.M., Ciucci, S., Bianconi, G., Cannistraci, C.V.: Machine learning meets complex networks via coalescent embedding in the hyperbolic space Nature Communications. 8, 1615 (2017).
2015[ to top ]
  • Daminelli, S., Thomas, J.M., Durán, C., Cannistraci, C.V.: Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks New Journal of Physics. 17, 113037 (2015).