Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs

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.

FDGNN: Fully Dynamic Graph Neural Network

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.

Attribute Point Processes

Graph Neural Networks Designed for Different Graph Types: A Survey

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.