Metadata about: Graph convolutional neural networks have been widely studied for semi-supervised classification on graph-structured data in recent years. They usually learn node representations by transforming, propagating, aggregating node features and minimizing the prediction loss on labeled nodes. However, the pseudo labels generated on unlabeled nodes are usually overlooked during the learning process. In this paper, we propose a soft labels guided graph attention network (SLGAT) to improve the performance of node representation learning by leveraging generated pseudo labels. Unlike the prior graph attention networks, our SLGAT uses soft labels as guidance to learn different weights for neighboring nodes, which allows SLGAT to pay more attention to the features closely related to the central node labels during the feature aggregation process. We further propose a self-training based optimization method to train SLGAT on both labeled and pseudo labeled nodes. Specifically, we first pre-train SLGAT on labeled nodes and generate pseudo labels for unlabeled nodes. Next, for each iteration, we train SLGAT on the combination of labeled and pseudo labeled nodes, and then generate new pseudo labels for further training. Experimental results on semi-supervised node classification show that SLGAT achieves state-of-the-art performance.

About: Graph convolutional neural networks have been widely studied for semi-supervised classification on graph-structured data in recent years. They usually learn node representations by transforming, propagating, aggregating node features and minimizing the prediction loss on labeled nodes. However, the pseudo labels generated on unlabeled nodes are usually overlooked during the learning process. In this paper, we propose a soft labels guided graph attention network (SLGAT) to improve the performance of node representation learning by leveraging generated pseudo labels. Unlike the prior graph attention networks, our SLGAT uses soft labels as guidance to learn different weights for neighboring nodes, which allows SLGAT to pay more attention to the features closely related to the central node labels during the feature aggregation process. We further propose a self-training based optimization method to train SLGAT on both labeled and pseudo labeled nodes. Specifically, we first pre-train SLGAT on labeled nodes and generate pseudo labels for unlabeled nodes. Next, for each iteration, we train SLGAT on the combination of labeled and pseudo labeled nodes, and then generate new pseudo labels for further training. Experimental results on semi-supervised node classification show that SLGAT achieves state-of-the-art performance.

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