09/02/2026
research article
Topology-aware GNNs under structural perturbations: Empirical robustness across domains
The authors study how graph neural networks behave when some edges in a graph are missing or altered. These models are widely used to classify molecules, proteins, and social networks, but their predictions can change if the observed connections are incomplete or noisy. They combine two complementary views of a graph: a standard message-passing network, and a topological summary.
Image credit: Uriel SC
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