The proposed framework leverages metric distortion to examine how topology awareness in Graph Neural Networks (GNNs) impacts their generalization performance, thus providing significant insight into model efficacy.
In our framework, we define a systematic approach that links the topology awareness of GNNs with their ability to generalize in various transductive learning scenarios, paving the way for advanced semi-supervised models.
#graph-neural-networks #topology-awareness #generalization-performance #semi-supervised-learning #machine-learning
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