Our results indicate a critical relationship between generalization performance and structural distance, offering insights into how topology awareness impacts the effectiveness of Graph Neural Networks (GNNs). This perspective enhances the understanding of factors that determine the performance of GNNs, specifically regarding their ability to generalize across different subgroups defined by structural characteristics.
Theorem 1 reveals that the generalization risk of a classifier pertaining to structural subgroups is intrinsically linked to the classifier's empirical training loss and the structural distance from the training set. This connection underscores the importance of topology awareness in GNNs, prompting deeper investigations into how these aspects influence performance disparities across varying structural groupings.
#graph-neural-networks #generalization-performance #structural-distance #topology-awareness #research-framework
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