When using GNNs for predictions, you can get tighter, more reliable uncertainty estimates by explicitly using graph structure rather than just embedding similarity—this gives you both statistical guarantees and practical efficiency.
GRAPHLCP improves uncertainty quantification for graph neural networks by using graph structure to make better predictions with guaranteed coverage. Instead of just looking at embedding similarity, it uses graph topology and a PageRank-based approach to identify similar nodes and weight predictions appropriately, reducing wasted prediction sets while maintaining statistical guarantees.