  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.
