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Due to the cost of labeling nodes, classifying a node in a sparsely labeledgraph while maintaining the prediction accuracy deserves attention. The keypoint is how the algorithm learns sufficient information from more neighborswith different hop distances. This study first proposes a hop-aware attentionsupervision mechanism for the node classification task. A simulated annealinglearning strategy is then adopted to balance two learning tasks, nodeclassification and the hop-aware attention coefficients, along the trainingtimeline. Compared with state-of-the-art models, the experimental resultsproved the superior effectiveness of the proposed Hop-aware Supervision GraphAttention Networks (HopGAT) model. Especially, for the protein-proteininteraction 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 experimentsalso demonstrate the effectiveness of supervised attention coefficient andlearning strategies.

HopGAT: Hop-aware Supervision Graph Attention Networks for Sparsely Labeled Graphs