  Graph convolutional networks have achieved great success on graph-structured
data. Many graph convolutional networks can be regarded as low-pass filters for
graph signals. In this paper, we propose a new model, BiGCN, which represents a
graph neural network as a bi-directional low-pass filter. Specifically, we not
only consider the original graph structure information but also the latent
correlation between features, thus BiGCN can filter the signals along with both
the original graph and a latent feature-connection graph. Our model outperforms
previous graph neural networks in the tasks of node classification and link
prediction on most of the benchmark datasets, especially when we add noise to
the node features.
