Graph neural networks (GNNs) have generalized deep learning methods into graph-structured data with promising performance on graph mining tasks . However, existing GNNs often meet complex graph structures with scarce labeled nodes and suffer from the limitations of non-robustness, over-smoothing, and overfitting. To address these issues , we propose a simple yet effective GNN framework---Graph Random Neural Network (Grand). Different from the deterministic propagation in existing GNNs, Grand adopts a random propagation strategy to enhance model robustness. This strategy also naturally enables Grand to decouple the propagation from feature transformation, reducing the risks of over-smoothing and overfitting. Moreover, random propagation acts as an efficient method for graph data augmentation. Based on this, we propose the consistency regularization for Grand by leveraging the distributional consistency of unlabeled nodes in multiple augmentations, improving the generalization capacity of the model . Extensive experiments on graph benchmark datasets suggest that Grand significantly outperforms state-of-the-art GNN baselines on semi-supervised graph learning tasks . Finally, we show that Grand mitigates the issues of over-smoothing and overfitting, and its performance is married with robustness.
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored . However, most existing GNNs inherently suffer from the limitations of over-smoothing, non-robustness, and weak-generalization when labeled nodes are scarce. In this paper , we propose a simple yet effective framework---GRAPH RANDOM NEURAL NETWORKS (GRAND)---to address these issues. In GRAND, we first design a random propagation strategy to perform graph data augmentation. Then we leverage consistency regularization to optimize the prediction consistency of unlabeled nodes across different data augmentations . Extensive experiments on graph benchmark datasets suggest that GRAND significantly outperforms state-of-the-art GNN baselines on semi-supervised node classification . Finally, we show that GRAND mitigates the issues of over-smoothing and non-robustness, exhibiting better generalization behavior than existing GNNs. The source code of GRAND is publicly available at URL
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We study the problem of semi-supervised learning on graphs, for which graph
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generalized deep learning methods into graph-structured data with promising performance on graph mining tasks
been extensively explored
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existing GNNs often meet complex graph structures with scarce labeled nodes and
most existing GNNs inherently
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non-robustness,
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and overfitting. To address these issues
non-robustness, and weak-generalization when labeled nodes are scarce. In this paper
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GNN framework---Graph Random Neural Network (Grand). Different from the deterministic propagation in existing GNNs, Grand adopts
framework---GRAPH RANDOM NEURAL NETWORKS (GRAND)---to address these issues. In GRAND, we first design
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enhance model robustness. This strategy also naturally enables Grand to decouple the propagation from feature transformation, reducing the risks of over-smoothing and overfitting. Moreover, random propagation acts as an efficient method for
perform
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Based on this, we propose the consistency regularization for Grand by leveraging the distributional
Then we leverage consistency regularization to optimize the prediction
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in multiple augmentations, improving the generalization capacity of the model
across different data augmentations
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Grand
GRAND
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graph learning tasks
node classification
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Grand
GRAND
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overfitting, and its performance is married with robustness.
non-robustness, exhibiting better generalization behavior than existing GNNs. The source code of GRAND is publicly available at URL