Pu­bli­ka­tio­nen

[ 2022 ] [ 2021 ]

2022 [ nach oben ]

  • 1.
    Moallemy-Oureh, A.: Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs. ACM/SIGAPP Symposium on Applied Computing (SAC). bll. 600–603. ACM (2022).
     
  • 2.
    Thomas, J.M., Moallemy-Oureh, A., Beddar-Wiesing, S., Holzhüter, C.: Graph Neural Networks Designed for Different Graph Types: A Survey. arXiv e-prints. arXiv:2204.03080 (2022).
     
  • 3.
    Moallemy-Oureh, A., Beddar-Wiesing, S., Nather, R., Thomas, J.M.: FDGNN: Fully Dynamic Graph Neural Network. arXiv e-prints. arXiv:2206.03469 (2022).
     

2021 [ nach oben ]

  • 1.
    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).