A GCN/GAT framework predicts IPR values of the principal eigenvector and classifies networks into delocalized, weakly localized, and strongly localized states with roughly 95 percent accuracy on synthetic test networks.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Predicting Steady-State Behavior in Complex Networks with Graph Neural Networks
A GCN/GAT framework predicts IPR values of the principal eigenvector and classifies networks into delocalized, weakly localized, and strongly localized states with roughly 95 percent accuracy on synthetic test networks.