  Graph Neural Networks (GNNs) have been widely applied in the semi-supervised
node classification task, where a key point lies in how to sufficiently
leverage the limited but valuable label information. Most of the classical GNNs
solely use the known labels for computing the classification loss at the
output. In recent years, several methods have been designed to additionally
utilize the labels at the input. One part of the methods augment the node
features via concatenating or adding them with the one-hot encodings of labels,
while other methods optimize the graph structure by assuming neighboring nodes
tend to have the same label. To bring into full play the rich information of
labels, in this paper, we present a label-enhanced learning framework for GNNs,
which first models each label as a virtual center for intra-class nodes and
then jointly learns the representations of both nodes and labels. Our approach
could not only smooth the representations of nodes belonging to the same class,
but also explicitly encode the label semantics into the learning process of
GNNs. Moreover, a training node selection technique is provided to eliminate
the potential label leakage issue and guarantee the model generalization
ability. Finally, an adaptive self-training strategy is proposed to iteratively
enlarge the training set with more reliable pseudo labels and distinguish the
importance of each pseudo-labeled node during the model training process.
Experimental results on both real-world and synthetic datasets demonstrate our
approach can not only consistently outperform the state-of-the-arts, but also
effectively smooth the representations of intra-class nodes.
