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E-GCN: graph convolution with estimated labels
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DOI：10.1007/s10489-020-02093-5.png)
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G raph C onvolutional N etwork (GCN) has been commonly applied for semi-supervised learning tasks. However, the established GCN frequently only considers the given labels in the topology optimization, which may not deliver the best performance for semi-supervised learning tasks. In this paper, we propose a novel G raph C onvolutional N etwork with E stimated labels (E-GCN) for semi-supervised learning. The core design of E-GCN is to learn a suitable network topology for semi-supervised learning by linking both estimated labels and given labels in a centralized network framework. The major enhancement is that both given labels and estimated labels are utilized for the topology optimization in E-GCN, which assists the graph convolution implementation for unknown labels evaluation. Experimental results demonstrate that E-GCN is significantly better than s tate-o f-t he-a rt (SOTA) baselines without estimated labels.
Keyword:
Semi-supervised
Learning graph convolution
Topology optimization
Estimated labels
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3.5
论文数:
7.6K
被引数:
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