  Due to the cost of labeling nodes, classifying a node in a sparsely labeled
graph while maintaining the prediction accuracy deserves attention. The key
point is how the algorithm learns sufficient information from more neighbors
with different hop distances. This study first proposes a hop-aware attention
supervision mechanism for the node classification task. A simulated annealing
learning strategy is then adopted to balance two learning tasks, node
classification and the hop-aware attention coefficients, along the training
timeline. Compared with state-of-the-art models, the experimental results
proved the superior effectiveness of the proposed Hop-aware Supervision Graph
Attention Networks (HopGAT) model. Especially, for the protein-protein
interaction network, in a 40% labeled graph, the performance loss is only 3.9%,
from 98.5% to 94.6%, compared to the fully labeled graph. Extensive experiments
also demonstrate the effectiveness of supervised attention coefficient and
learning strategies.
