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.
@article{arxiv.2104.02153,
title = {Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional Networks},
author = {Claudio Bellei and Hussain Alattas and Nesrine Kaaniche},
journal= {arXiv preprint arXiv:2104.02153},
year = {2021}
}