Pub­lic­a­tions

[ 2022 ] [ 2021 ] [ 2017 ] [ 2015 ]

2022 [ to top ]

  • 1.
    Decke, J., Engelhardt, A., Rauch, L., Degener, S., Sajjadifar, S., Scharifi, E., Steinhoff, K., Niendorf, T., Sick, B.: Predicting flow stress behavior of an AA7075 alloy using machine learning methods. Crystals. 9, 1–19 (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 [ to top ]

  • 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).
     

2017 [ to top ]

  • 1.
    Durán, C., Daminelli, S., M., T.J., Haupt, V.J., Schroeder, M., Cannistraci, C.V.: Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory. Briefings in Bioinformatics. 19, 1183–1202 (2017).
     
  • 2.
    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 ]

  • 1.
    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).