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

• URL: http://arxiv.org/abs/2104.02153v1
• Date: Mon, 5 Apr 2021 21:02:48 GMT
• Title: Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional Networks
• Authors: Claudio Bellei, Hussain Alattas, and Nesrine Kaaniche
• Abstract summary: 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. This is done by selectively eliminating self-loops for the label features during the training phase of a GCN. 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.
• Score: 1.8352113484137624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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