  Graph convolutional networks(GCNs) have become the most popular approaches
for graph data in these days because of their powerful ability to extract
features from graph. GCNs approaches are divided into two categories,
spectral-based and spatial-based. As the earliest convolutional networks for
graph data, spectral-based GCNs have achieved impressive results in many graph
related analytics tasks. However, spectral-based models cannot directly work on
directed graphs. In this paper, we propose an improved spectral-based GCN for
the directed graph by leveraging redefined Laplacians to improve its
propagation model. Our approach can work directly on directed graph data in
semi-supervised nodes classification tasks. Experiments on a number of directed
graph datasets demonstrate that our approach outperforms the state-of-the-art
methods.
