In this work, we study and investigate GNNs, exploring the relationship between topology awareness and generalization performance in semi-supervised node classification tasks.
Our proposed framework connects the structural awareness of GNNs with approximate metric embedding, providing fresh insights into GNN generalizability across varied graph structures.
A case study on graph distance indicates our theoretical findings are mirrored in the practical generalization performance of GNNs, impacting significant applications.
Our framework introduces a new perspective on GNN generalization but has limitations, particularly in how it interprets embeddings and the dynamics of reduced distortion.
#graph-neural-networks #topology-awareness #generalization-performance #machine-learning #computer-vision
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