Core Concepts
The author proposes the KuramotoGNN, a continuous-depth GNN inspired by the Kuramoto model, to address over-smoothing by leveraging insights from synchronization. The model demonstrates superior performance compared to other GNN variants.
Abstract
The content introduces the KuramotoGNN as a solution to the over-smoothing problem in Graph Neural Networks (GNNs). By drawing parallels between synchronization in coupled oscillators and over-smoothing in GNNs, the author presents theoretical analysis and empirical results showcasing the effectiveness of KuramotoGNN. The model is evaluated on various benchmarks, demonstrating resilience to deep layers and outperforming other GNN architectures.
Stats
Mean accuracy of KuramotoGNN on CORA: 85.18%
Mean accuracy of GRAND++ on Photo: 93.55%
Quotes
"Over-smoothing is the phase synchronization state of the node features."
"Our work provides a promising direction for addressing the over-smoothing problem in GNNs."