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Graph neural networks (GNNs) are designed for semi-supervised node classification on graphs where only a subset of nodes have class labels.
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Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes,
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Simple and deep graph convolutional networks,
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Deeper insights into graph convolutional networks for semi-supervised learning,
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking,
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Encoding social information with graph convolutional networks forpolitical perspective detection in news media,
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Graph neural networks for social recommendation,
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Label-consistency based graph neural networks for semi-supervised node classification,
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Semi-supervised classification by graph p-laplacian convolutional networks,
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On region-level travel demand forecasting using multi-task adaptive graph attention network,
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Sociallgn: Light graph convolution network for social recommendation,
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Joint hyperbolic and euclidean geometry contrastive graph neural networks,
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Hapgnn: Hop-wise attentive pagerank-based graph neural network,
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Informative pseudo-labeling for graph neural networks with few labels,
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Negative samples selecting strategy for graph contrastive learning,
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