  We propose a novel neural network architecture, called
autoencoder-constrained graph convolutional network, to solve node
classification task on graph domains. As suggested by its name, the core of
this model is a convolutional network operating directly on graphs, whose
hidden layers are constrained by an autoencoder. Comparing with vanilla graph
convolutional networks, the autoencoder step is added to reduce the information
loss brought by Laplacian smoothing. We consider applying our model on both
homogeneous graphs and heterogeneous graphs. For homogeneous graphs, the
autoencoder approximates to the adjacency matrix of the input graph by taking
hidden layer representations as encoder and another one-layer graph
convolutional network as decoder. For heterogeneous graphs, since there are
multiple adjacency matrices corresponding to different types of edges, the
autoencoder approximates to the feature matrix of the input graph instead, and
changes the encoder to a particularly designed multi-channel pre-processing
network with two layers. In both cases, the error occurred in the autoencoder
approximation goes to the penalty term in the loss function. In extensive
experiments on citation networks and other heterogeneous graphs, we demonstrate
that adding autoencoder constraints significantly improves the performance of
graph convolutional networks. Further, we notice that our technique can be
applied on graph attention network to improve the performance as well. This
reveals the wide applicability of the proposed autoencoder technique.
