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arXiv · 2104.02153

Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional Networks

Claudio Bellei·Hussain Alattas·Nesrine Kaaniche
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Abstract

We show that a modification of the first layer of a Graph Convolutional Network (GCN) can be used to effectively propagate label information across neighbor nodes, for binary and multi-class classification problems. This is done by selectively eliminating self-loops for the label features during the training phase of a GCN. The GCN architecture is otherwise unchanged, without any extra hyper-parameters, and can be used in both a transductive and inductive setting. We show through several experiments that, depending on how many labels are available during the inference phase, this strategy can lead to a substantial improvement in the model performance compared to a standard GCN approach, including with imbalanced datasets.

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Claudio Bellei, Hussain Alattas, Nesrine Kaaniche. 2021-04-05. Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional Networks. https://arxiv.org/abs/2104.02153

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