  Representing a label distribution as a one-hot vector is a common practice in
training node classification models. However, the one-hot representation may
not adequately reflect the semantic characteristics of a node in different
classes, as some nodes may be semantically close to their neighbors in other
classes. It would cause over-confidence since the models are encouraged to
assign full probabilities when classifying every node. While training models
with label smoothing can ease this problem to some degree, it still fails to
capture the nodes' semantic characteristics implied by the graph structures. In
this work, we propose a novel SALS (\textit{Structure-Aware Label Smoothing})
method as an enhancement component to popular node classification models. SALS
leverages the graph structures to capture the semantic correlations between the
connected nodes and generate the structure-aware label distribution to replace
the original one-hot label vectors, thus improving the node classification
performance without inference costs. Extensive experiments on seven node
classification benchmark datasets reveal the effectiveness of our SALS on
improving both transductive and inductive node classification. Empirical
results show that SALS is superior to the label smoothing method and enhances
the node classification models to outperform the baseline methods.
