1 code implementation • 8 Oct 2022 • Silvia Beddar-Wiesing, Giuseppe Alessio D'Inverno, Caterina Graziani, Veronica Lachi, Alice Moallemy-Oureh, Franco Scarselli, Josephine Maria Thomas
In this paper, we conduct a theoretical analysis of the expressive power of GNNs for two other graph domains that are particularly interesting in practical applications, namely dynamic graphs and SAUGHs with edge attributes.
no code implementations • 7 Jun 2022 • Alice Moallemy-Oureh, Silvia Beddar-Wiesing, Rüdiger Nather, Josephine M. Thomas
The few GNN models on dynamic graphs only consider exceptional cases of dynamics, e. g., node attribute-dynamic graphs or structure-dynamic graphs limited to additions or changes to the graph's edges, etc.
no code implementations • 6 Apr 2022 • Josephine M. Thomas, Alice Moallemy-Oureh, Silvia Beddar-Wiesing, Clara Holzhüter
Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture.
no code implementations • 13 Jan 2020 • Silvia Beddar-Wiesing, Maarten Bieshaar
Fusion is a common tool for the analysis and utilization of available datasets and so an essential part of data mining and machine learning processes.