Title: Graph Convolutional Networks for Classification with a Structured Label Space,
Abstract: It is a usual practice to ignore any structural information underlying
classes in multi-class classification. In this paper, we propose a graph
convolutional network (GCN) augmented neural network classifier to exploit a
known, underlying graph structure of labels. The proposed approach resembles an
(approximate) inference procedure in, for instance, a conditional random field
(CRF). We evaluate the proposed approach on document classification and object
recognition and report both accuracies and graph-theoretic metrics that
correspond to the consistency of the model's prediction. The experiment results
reveal that the proposed model outperforms a baseline method which ignores the
graph structures of a label space in terms of graph-theoretic metrics.