  The graph convolution network (GCN) is a widely-used facility to realize
graph-based semi-supervised learning, which usually integrates node features
and graph topologic information to build learning models. However, as for
multi-label learning tasks, the supervision part of GCN simply minimizes the
cross-entropy loss between the last layer outputs and the ground-truth label
distribution, which tends to lose some useful information such as label
correlations, so that prevents from obtaining high performance. In this paper,
we pro-pose a novel GCN-based semi-supervised learning approach for multi-label
classification, namely ML-GCN. ML-GCN first uses a GCN to embed the node
features and graph topologic information. Then, it randomly generates a label
matrix, where each row (i.e., label vector) represents a kind of labels. The
dimension of the label vector is the same as that of the node vector before the
last convolution operation of GCN. That is, all labels and nodes are embedded
in a uniform vector space. Finally, during the ML-GCN model training, label
vectors and node vectors are concatenated to serve as the inputs of the relaxed
skip-gram model to detect the node-label correlation as well as the label-label
correlation. Experimental results on several graph classification datasets show
that the proposed ML-GCN outperforms four state-of-the-art methods.
