Graph Convolutional Networks for Classification with a Structured Label Space

Understand

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.